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Pavan Vadapalli

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Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

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Top 19 Java 8 Interview Questions (2023)
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Top 19 Java 8 Interview Questions (2023)

Java 8: What Is It? Let’s conduct a quick refresher and define what Java 8 is before we go into the questions. To increase the efficiency with which Java applications may be developed and operated, Java has published Java 8, which was made available to the public on March 14, 2014. Java is undeniably one of the first names that come to our mind when we talk about software development. A primary driving power behind the global digital lifestyle, Java is an object-oriented programming (OOP) language developed by James A. Gosling in the mid-1990s, together with Patrick Naughton and Mike Sheridan.  With its many perks that include platform independence, object-oriented nature, and ease of learning, Java finds a host of applications in the software industry; be it Android applications, web-based applications, or desktop GUI applications, computers without Java are legit unimaginable.  The latest addition to the incredible functionalities of Java is Java 8, an open-source project released on March 18, 2014. Regarded as a revolutionary release for software development platforms, Java 8 is packed with upgrades and new features to the Java programming, tools, libraries, and JVM. The Oracle Corporation currently manages it. Needless to say, Java 8 interview questions are a hot topic in the field of software development. Moreover, if you are a fresher looking to make it big as a software developer or a related job title, let us walk you through the top Java 8 interview questions and answers you should go through as part of your D-day prep. In addition, we will talk about some popular upGrad courses that will help you get a step closer to your dream job! Why do you think Java SE 8 is so beneficial? It has been decided that programmers would benefit greatly from Java SE 8’s release. In particular, these are: It simplifies and clarifies the code. The addition of Lambda Expressions and Functional Interfaces to Java SE 8 increased code reusability. With Java 8, it’s easier to keep up with updates and run tests on code. It aids in the creation of incredibly scalable code and interfaces. Aids in the creation of alternative programs. This facilitated the creation of database-like activities. It boosts the efficiency of a wide range of programs. Java SE 8 allows for more efficient programming. Top 15 Java 8 Interview Questions and Answers 1. What are the new features added to Java 8? The essential features included in Java 8 include: Functional interfaces Lambda expression Method references Stream API Date time API Optional Default method Nashorn, JavaScript Engine 2. What are functional interfaces? Functional interfaces are interfaces that have only one abstract method. These interfaces are implemented using a Lambda expression. Thus, to use a Lambda expression, we need to create a new functional interface or use the predefined one of Java 8. 3. What are the characteristics of the Lambda function? Main characteristics of the Lambda function include: A method defined as Lambda expression can be passed as a parameter to a different method. There is no requirement of declaring the parameter type since the compiler can fetch the same from the parameter’s value. A method can exist independently without belonging to a class. Using a single parameter does not require parentheses, but the same can be done using multiple parameters. There is no need to use curly braces if the body of expression has only a single statement. 4. What is the structure of the Lambda expression? Lambda expression has the following structure: (Argument List) ->{statements;} or (Argument List) ->{expression;} 5. What are method references? Method references in Java 8 are a particular type of Lambda expression used for creating simple Lambda expressions by referencing existing methods. The different kinds of method references are – reference to a static method, reference to a constructor, and reference to an instance method. 6. What are Default Methods in Java 8? Default methods are those methods of the interface which have a body and utilize default keywords. Default methods have the property of backward compatibility. It means that if we add the default method in an interface, we will be able to provide the default implementation without affecting the implementing classes. However, if JDK modifies any interface that does not have the default method, then the classes that implement the said interface will break. 7. What is an optional class? Optional classes are special wrapper classes in Java 8 for avoiding NullPointerExceptions. NullPointerExceptions occur when we fail to perform Null checks. Optional classes encapsulate optional value that is either present or not. 8. Give some examples of standard Java pre-defined functional interfaces? The functional interfaces introduced by Java 8 include Predicate, Consumer, Supplier, etc. But popular pre-defined functional interfaces from earlier Java versions include the following: Runnable: For executing the instances of a class over another thread with no return value and no arguments. Comparable: For sorting objects in the natural sort order. Comparator: For sorting different objects as per user-defined order. Callable: For executing the instances of a class over another thread with no arguments. It either throws an exception or returns a value. 9. Name the various categories of pre-defined functional interfaces and their uses. The various categories of pre-defined functional interfaces are: Function: For transforming arguments in returnable value. Operator: For performing reduction type operations that accept the same input types. Consumer: Accepts arguments but does not return any values. Supplier: Returns a value but does not accept any arguments. Predicate: For performing a test and returning a boolean value. 10. What are some date and time APIs in Java 8? LocalTime, LocalDate, and LocalDateTime are the core date and time API classes in Java 8. These classes are local to the context of the observer and denote the current time and date in the context of the observer. 11. What were the drawbacks of the old date and time? The old date and time suffered from the following limitations: Java 8 time and date API meet the ISO standards, but the old date and time had a poor design. While the latest Java 8 date and time API is thread-safe, Java.util.Date was mutable and not thread-safe. In terms of performance, Java 8 is faster than the older date and time regime. 12. What is the difference between a Predicate and a Function? A Predicate and a Function differ in the following respects: While Predicate has the return type as boolean, Function has the return type as the object. A Predicate is written as Predicate<T> and accepts a single argument. A Function also accepts a single argument but is written as Function<T,R> Here, T denotes the type of input to the function, and R represents the type of result. 13. What are Stream APIs, and why do we need them? Stream APIs are a new feature in Java 8, a special class used for processing objects from a source such as Collection. Stream APIs are needed for: Supporting aggregate operations and simplifying processing Faster processing and better performance Supporting functional-style programming Parallel operations 14. What is the use of the limit() method in Java 8? The Stream.limit() method is used for specifying the limit of the elements. If we specify the size of the limit(X), it will return the Stream of size ‘X.’ 15. What are PermGen and MetaSpace? Up to Java 7, PermGen was an area used by JVM to store classes. PermGen was replaced by MetaSpace in Java 8. The major benefit of MetaSpace over PermGen is in terms of size. While PermGen had a fixed maximum size and could not grow dynamically, MetaSpace does not have any size constraint and is capable of dynamic growth. 16. What is Optional in Java 8? The Optional object is a container for other, non-null objects. Null without a value is represented as a non-mandatory object.  The Optional class provides many helper methods that may be used in place of testing for null values, allowing for more flexible handling of items that may or may not be accessible.  Instead of utilizing null references, the Optional class provides a type-level alternative for encoding optional data. 17. What is a Static Method? Why do we need Static methods in Java 8 Interfaces? Static methods are connected with a certain class or interface and might be either utility or helper methods. It’s not connected to anything in particular. The following justify our requirement for Static Methods: Instead of placing interface-specific utility methods in a separate Utility class, we may maintain them in the interface itself. There is no need to retain Utility Methods in specialized Utility Classes like Collections, Arrays, etc. Separation of Duties that is easy to understand. That is, there is no need for a separate Utility class to house the many Utility operations available in the Collections API. The API may be easily extended. The API is simple to keep up with. 18. Do you have any background working with Java in a real-world setting? If you are a seasoned programmer going in for an interview, you should expect this question. To the best of your ability, please respond with an honest account of your prior work in a production setting supporting Java development. 19. Is there a particular Java Developer certification you hold that sets you apart from the competition? As well as giving you an in-depth understanding of the technology you wish to master, a certification from a reputable organization will look great on your CV. You may show the interviewer that you have put in the time and effort necessary to understand the technology by working on real-world projects at the industry level, which are included in the certification process. Certification in a particular technology will lend legitimacy to your resume. It will give you a leg up in terms of information, education, and preparedness for interviews. Here’s What You Can Do Next Preparing for tech interviews can be a real hassle if you are not prepared well. By preparation, we do not simply mean going through truckloads of mock questions but also ensuring that your fundamentals are clear as crystal. And what better way to taking the first step towards your goals than signing up with upGrad? upGrad is a leading online higher education platform offering industry-relevant programs and courses in collaboration with the best-in-class faculty and experts. With top university partners including Liverpool John Moores University, IIIT Bangalore, IIT Madras, and Duke Corporate Education, upGrad amalgamates the best teaching practices and latest technology to provide an immersive and world-class learning experience. upGrad’s educational programs and courses have impacted over 500,000 working professionals. In addition, it has a paid learner base of over 40,000 spread across more than 85 countries. If you wish to pursue a career in software development or related fields, upGrad’s Master of Science in Computer Science and Executive PG Program in Software Development are two courses you should check out. With upGrad 360-degree career support, peer learning opportunities, and industry networking, the unique programs are curated to help learners master the know-how of software development.  1. Master of Science in Computer Science (19 months) Program Highlights: Master’s degree from the prestigious Liverpool John Moores University. Six unique specializations to choose from – Big Data, Full Stack Development, Blockchain Development, DevOps, Cloud Computing, and Cyber Security. Comprehensive coverage of 30+ programming languages, tools, and software with four years of unlimited content access. More than 500 hours of content with 30+ case studies and projects, 10+ live sessions, and industry mentorship 2. Executive PG Program in Software Development (13 months) Program Highlights: Recognition from the prestigious IIIT Bangalore. Six unique specializations to choose from – Big Data, Full Stack Development, Blockchain Development, DevOps, Cloud Computing, and Cyber Security. Hands-on exposure to over 30 industry-relevant assignments, case studies, and projects with coverage of 30+ tools and software. 400+ hours of content, over ten live sessions, and mentorship from leading faculty and industry experts. Gearing Up for the Challenge: Java 8 Coding Interview Questions for Freshers & Experienced and Practical Tests Java 8 interviews often delve beyond theoretical knowledge, assessing your practical skills and problem-solving prowess through diverse Java 8 coding practice problems and tests. Here’s a roadmap to navigate these assessments: Showcasing Your Command of Java 8: Lambda Expressions, Streams, and Functional Interfaces: Be prepared to demonstrate your understanding by tackling Java 8 programming questions specifically designed to leverage these powerful features. Algorithmic Expertise: Interviewers may present algorithmic challenges, from sorting and searching to collection manipulation, requiring solutions crafted with Java 8 features. So, prepare yourself for java 8 features interview questions. Design and Architecture for Real-World Scenarios: Design or architecture challenges based on realistic situations might test your ability to propose solutions leveraging Java 8 concepts (classes, interfaces, component interactions). Refactoring Magic: Breathe new life into code snippets written in older Java versions by refactoring them with efficiency and readability enhancements using Java 8 features. Debugging and Error Sleuthing: Put your detective skills to the test by identifying and fixing bugs or errors in provided code, applying your Java 8 knowledge and syntax. Sharpen Your Skills with Practice: Regularly engaging with Java 8 interview coding questions and practical tests is key to honing your Java 8 skills and boosting your interview confidence. Portfolio and Projects: Beyond the Resume Java 8 interviews often delve into your real-world experience through portfolio and project discussions. Be ready to address these topics: Project Scope and Impact: Articulate the scope and objectives of your past projects, highlighting the problems you tackled and the requirements you met. Tech Stack Savvy: Explain the technologies and frameworks you employed, emphasizing any Java 8 features you strategically leveraged. Conquering Challenges: Share any obstacles you encountered during your projects and the solutions you devised, showcasing your problem-solving approach and resilience. Success Stories: Highlight achievements like meeting deadlines, exceeding performance targets, or receiving positive feedback, demonstrating your value contribution. Lessons Learned: Reflect on the knowledge you gained from your projects, including insights from mistakes and how you’d approach similar projects differently. By delving into your portfolio and projects, you can weave a compelling narrative that showcases your experience, skills, and achievements to potential employers. Common Stumbles to Avoid: Pitfalls in Java 8 Interview Questions Even seasoned developers can make missteps in Java 8 interviews. Here are some common pitfalls to steer clear of: Unpreparedness: Incomplete understanding of core concepts and inadequate practice can hinder your ability to answer Java 8 interview questions and demonstrate skills. Shaky Java 8 Foundation: Ensure you have a solid grasp of lambda expressions, streams, and functional interfaces to avoid errors during coding challenges or tests. Ignoring Edge Cases: Overlooking boundary conditions and edge cases in coding problems or algorithm design can lead to inaccurate results or unexpected behavior. Communication Barriers: Convey your thought process and reasoning clearly and concisely to avoid misunderstandings. Poor communication can misrepresent your abilities. Lack of Confidence: Believe in yourself! Project confidence and self-assurance to make a positive impact on the interviewer. By being aware of these common pitfalls and actively preparing, you can confidently navigate Java 8 interview questions and increase your chances of landing your dream job. Feedback and Continuous Learning: The Journey Doesn’t End The interview process doesn’t conclude with the final handshake. Feedbacks are important even for java 8 interview questions for 5 years, 10 years experienced. Here’s how to make the most of the feedback and continue your learning journey: Self-Reflection: A Valuable Tool: Take time to reflect on your interview experience, identifying areas of strength, weakness, and opportunities for improvement. This introspection will guide your future preparation. Seeking Feedback: Don’t hesitate to reach out to interviewers for constructive feedback. Their insights can be invaluable for honing your Java 8 programming questions skills. Embrace Continuous Learning: The world of Java 8 is constantly evolving. Make continuous learning and skill development a top priority. Explore new features, practice coding challenges, and embark on personal projects. Stay Ahead of the Curve: Actively engage with the Java 8 community by reading blogs, attending conferences, and participating in online forums. Staying up-to-date will keep you competitive in the job market. Additional Tips for Success Practice Interviewing: Participating in mock interviews with friends, colleagues, or online platforms can help you become comfortable answering questions under pressure and articulate your thought process effectively. Showcase Your Passion: Express your genuine enthusiasm for Java 8 and its capabilities. Your passion can be contagious and leave a positive impression on the interviewer. Ask Relevant Questions: Prepare thoughtful questions to ask the interviewer about the company, team, and role. This demonstrates your interest and initiative. Follow Up: Thank the interviewer for their time and reiterate your interest in the position. You can also use this opportunity to clarify any points or highlight skills you may not have had a chance to mention in the interview By mastering Java 8 coding questions, showcasing your projects effectively, and avoiding common pitfalls, you can confidently approach any Java 8 interview. Remember, continuous learning, preparation, and a positive attitude are key to making a strong impression and landing your dream job. Conclusion Having a relevant academic degree is the first step towards a bright career in software development. upGrad’s robust courses in Computer Science and Software Development are designed to prepare candidates for varied job roles, including that of Software Developer, Big Data Developer, Data Engineer, Big Data Engineer, and Big Data Architect. The next step is preparing for the job interviews, where questions on the various programming languages and tools are pretty typical. Java being the programming language of choice for developers across multiple platforms, Java 8 interview questions are mandatory in the interviewee’s preparation agenda. 

by Pavan Vadapalli

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27 Feb 2024

Artificial Intelligence in Banking 2024: Examples &#038; Challenges
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Artificial Intelligence in Banking 2024: Examples & Challenges

Introduction Millennials and their changing preferences have led to a wide-scale disruption of daily processes in many industries and a simultaneous growth of many more in other sectors. Much like hand soaps and cereals, the use of a physical bank location has declined. Physical bank locations may soon be a thing of the past, as per a report from Business Insider. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification With the customer preferences that are changing, the industries are adopting newer methods to match the pace of changing demands. Banking is digitizing as the word spreads. There is evident incorporation of operational process flows with artificial intelligence, robotics, and other machine assistance. The banking and financial sector today is continuously battling to reduce liabilities and increase assets. To provide systematic compliance management and operations, a fast-track strategy is required. Artificial intelligence (AI) is a key component of the banking and financial industries, helping to deliver affordable and dependable banking services. With a predicted CAGR of 32.6% from 2021 to 2030, the market for AI in banking Sector, which was valued at $3.88 billion in 2020, is expected to reach $64.03 billion by 2030. The financial landscape is experiencing a metamorphosis as artificial intelligence (AI) reshapes the very core of banking operations. From intelligent chatbots delivering personalized service to sophisticated algorithms predicting market trends, AI is revolutionizing the way banks interact with customers and navigate the competitive landscape. Let’s embark on a journey to explore the captivating world of AI in banking, delving into its transformative artificial intelligence applications in banking and real-world examples. Technology and the fourth industrial revolution have penetrated its way into many sectors. This technology is now reconstructing social skills and the workforce. Not only limiting the existence of a changing workforce, but the use of artificial intelligence is very evident in the banking sector. Artificial intelligence applications are not just modernising the banking sector but the entire world as we know of. Read more about the top artificial intelligence applications. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Why Use AI Technology is the face of this generation. To all the problems this generation has- there is a rising demand for answers. And, the solutions are sought after at the tip of their fingers. The other side of the screen might be a computer solving queries or a human employed as a relationship manager. Big data is the industry standard today, and every sector is working on grasping all that it could from the repositories of unstructured data. Big data applications in banking are already transforming the industry. Here comes artificial intelligence. Not only utilizing the benefits of AI in extracting and structuring the data in hand, finance, and banking sectors are stepping in to use this data to improve customer relations. Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Banking and AI Artificial intelligence is being used in the banking industry to scale new heights in customer relationship management. This sector is implementing this from the ground level with a principal aim of climbing heights in customer-centric approaches. A significant part of the banking industry concerning its customers is customer relationship management, which includes communicating with them. Banking saw a shift in preferences for visiting the locations with the introduction of ATMs. These machines allow cash deposit and withdrawal directly communicating with input points on the device, thus, not requiring human assistance at all. It was a revolution that led to the growth and demand for artificial intelligence. Artificial intelligence (AI) technology is being used more and more by banks and other financial institutions for a variety of purposes, such as improving customer service through the use of virtual assistants or credit scoring to correctly determine a borrower’s risk. But the battle against fraud and money laundering is one of the most significant applications of AI in banking sector. Digitization and Cyberthreats Banking is evolving in terms of digitization. Net banking, mobile banking, real-time money transfers, and similar services have changed the face of the sector from the last decades. With this digitization, there is an increase in the cyberthreat that comes along. These services again need to be secured from cybercriminal activities to ensure trust and safe transactions amongst users. With the availability of the right support, banks face difficulties in terms of the right workforce to drive the industry needs in the right direction. When sectors like banking, telecom, and information technology come together, the world witness’s plethora of valuable user- information on the world wide web. Every report of any user is as vulnerable as it is secured. Cybercrimes lead to disruption in the practices, and hence there have been strict regulations from government bodies to improve the banking industry’s adequacy to retain this massive data it has. Banks can benefit from digitalization thanks to artificial intelligence (AI), which also enables them to compete with FinTech companies. For instance, 32% of banks are currently utilizing AI technologies, such as predictive analytics, speech recognition, and other ones, to get a competitive edge in the market, according to a joint study by the National Business Research Institute and Narrative Science in 2020. Application Areas Artificial Intelligence is working to personalize human experiences with machines. Robots replacing the front-office staff in the banking sector are aimed to provide a 24*7 uninterrupted, diligent, and undeterred expertise to the customer in front. Banking today is witnessing a collaboration between humans and machines. This collaboration again is opening doors to customized opportunities for better service encounters and delivery. Benefits Artificial Intelligence in finance gives banks the ability to manage massive amounts of data at breakneck speeds to get insightful knowledge and better understand their clients’ behaviours. Due to the ability to offer customized features and easy interactions, artificial intelligence in finance is now able to tailor financial goods and services, resulting in significant consumer engagement and the development of solid client relationships. Restructuring reasons for the description, the following are the benefits in use:         Improved service responses         Reduction in human error         Personalized options in the making         Strengthening customer base by increasing satisfaction and trust         Reducing time to travel locations Banks are capturing the artificial intelligence by administering it into daily operational workflow by including changes in the values, employment and information patterns. Some of the application areas of artificial intelligence in the banking industry are listed as follows: 1. Refining Consumer Participation Artificial intelligence helps understand the customers better. The data gathered from the customer’s choices and preferences enable AI to lead machines to decode the next decisions and thus create a personalized container of information for each customer. This, in turn, is helpful for the banks to customize the buyer experiences as per their choices, in turn improving satisfaction and loyalty towards the institute. Interactive Voice Response System (IVRS) are examples of such AI-led systems that include voice assistance to customers. It guides the customers by understanding their queries in the right direction by routing calls to the correct department as well as assisting them with the transaction and other banking-related issues in real-time. 2. Wealth Supervision These customized plans for customers not only benefit the banks by increasing their customer-base but also helps the user to manage their wealth in hand with personalized inputs and advice on risk and investment plans. Involving AI-led customer service to meet the front office standards is a challenge with the diverse language set in countries like India. 3. Examining Data to Enhance Defence AI has the power to foretell future trends by interpreting data from the past. This property, when associated with machine learning, will help produce data-driven predictions to counter cases of capital laundering and identifying fraud. 4. Upgrading Security Unusual data pattern recognizing property of AI-led machines helps banks tighten security and recommend changes by identifying loopholes in existing processes. Deceptive emails and log reports, patterns in breach of process flows can be tracked by artificial intelligence to provide better security in the existing methods. 5. Interfacing Emotions AI-led machines use technology that identifies the emotions of the customers based on the text they use to input requirements. Based on this, the devices respond, suiting the tonality and fabrication of the words used by the customer. Natural language processing helps this happens. Read more about the applications of natural language processing. This not only a realistic experience but also helps banks save massive costs on human resources and large chunks of time. Chatbots are examples of AI in banking that are replacing the front-desk scenes at the banks. These AI-led machines provide next level digitized and customized interactive experiences to the customers. Learn more about creating a chatbot using Python. 6. Utilizing Knowledge Database AI-led systems in the banking sector is a massive treasury of data. It has all the details there is for every user on board. This database provides for more meticulous decision making based on improving strategic and business plan models. The AI-led repository is equivalent to a human expert on cognitive thinking. Face-detection and real-time cameras in ATMs and other such interventions is helping banks heighten measures into security and providing a clear and crisp insight into user’s behaviour patterns and techniques in operation. 7. Controlling Risks The vast data bank available from AI-powered systems allows the banks to manage risk by analysing their plans, studying failures from previous strategies, and eliminating human errors. AI is expanding into the roots of banking security processes to encrypt each step with codes that authenticate transactions, provide understanding to the companies on anti-fraud and anti-money-laundering activities. Regulatory checks like Know Your Customers (KYCs) help heightens security measures. 8. Expanding Through Front-office By offering to be personalized financial guides to customers and strengthening security against fraudulent activities, artificial intelligence is paving its path, strengthening not only in the front-office operation (customer interactions) but into the middle-office(security) and back-end development (underwriting banking service applications) as well. 9. Chatbots Chatbots powered by machine learning (ML) algorithms are at the forefront, providing personalized assistance 24/7. These virtual assistants answer queries, assist with account management, and even facilitate transactions, enhancing customer experience while reducing response times. Beyond customer service, AI algorithms analyze vast amounts of financial data in real-time, aiding in: 10. Tracking Market Trends Identifying investment opportunities and risks by discerning patterns and forecasts, keeping banks ahead of the curve. 11. Regulatory Compliance Automating processes, monitoring transactions, and detecting suspicious activities to ensure adherence to complex regulations, minimizing compliance risks. 12. Predictive Analytics Anticipating customer needs, optimizing product offerings, and mitigating risks using insights gleaned from historical data analysis, driving profitability and customer satisfaction. 13. Credit Scoring and Risk Assessment Analyzing vast datasets to assess creditworthiness, predict default risks, and determine loan eligibility, streamlining loan approvals, improving portfolio management, and increasing lending accessibility. 14. AI and Blockchain Enhancing security, scalability, and efficiency in banking operations by analyzing blockchain data, detecting anomalies, verifying transactions, and automating smart contract execution, fostering transparency and trust in financial transactions. Challenges Faced The financial services industry has seen a surge in artificial intelligence (AI) investments, which has raised new concerns about data security and transparency. As data management techniques change in response to the introduction of new AI solutions, these and other difficulties of AI in financial services are especially crucial to overcome. Organizations need to be aware of the upcoming difficulties listed below and implement safety measures to maintain progress. 1. Many banks face the challenge of an unwillingness to improve or adapt to new methods. Standardized with set practices in conventional ways, some locations in tier two and three cities across the country face this challenge. These units also lack the level of commitment required to upskill their labour force and human resources skills. 2. With the lack of supporting data to implement operational changes, the banking sector is facing a disconnect between the need and response from customers. The banks adapt to a switch that fails to comply with the actual requirement of the masses. 3. Banks with upscaling use of artificial intelligence need to keep up with the regulatory standards of government. The increasing services like net-banking and online transactions come under the ambit of privacy regulation policies as well, which necessitates compliance from the bank’s end. 4. There is also an evident lack of training witnessed in the existing workforce associating with the advanced tools and applications of the use of AI in banking. With the increasing use of artificial intelligence, there is an apparent demand for a skilled workforce. Proficient and experienced engineers in streams like data science and machine learning are needed to provide credibility to the data in hand. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Real-World Examples: The Power of AI in Action Several compelling use cases demonstrate the transformative impact of artificial intelligence in banking sector: Fraud Detection and Prevention: Banks leverage AI to detect anomalies in real-time, safeguarding customer assets from fraudulent activities, fostering trust and financial security. Customer Service Chatbots: AI-powered chatbots provide instant support, answer queries, and handle basic transactions, enhancing user experience, freeing up human agents for more complex issues, and improving overall customer satisfaction. Personalized Recommendations: AI tailors product recommendations based on individual preferences and financial goals, boosting customer satisfaction, loyalty, and driving cross-selling opportunities. Algorithmic Trading: AI enables high-frequency trading based on market data and predictions, allowing banks to capitalize on market fluctuations, enhance returns, and stay competitive. Credit Scoring and Loan Underwriting: AI analyzes diverse data sources for faster and more accurate loan approvals, minimizing defaults, optimizing lending portfolios, and promoting financial inclusion. RPA (Robotic Process Automation): Bots automate repetitive tasks, reducing operational costs, improving efficiency, and freeing up human resources for more strategic tasks. Sentiment Analysis: AI monitors social media and news for brand mentions and feedback, enabling proactive customer service, reputation management, and risk mitigation. Embracing the Future: A Symbiotic Relationship Use of AI and banking is not about replacing human expertise, but rather about augmenting it. By automating mundane tasks and providing data-driven insights, AI empowers human professionals to focus on strategic decision-making, delivering exceptional customer service, and fostering stronger relationships. As technology continues to evolve, use of generative ai in banking promises to drive further innovation, efficiency, personalized experiences, and inclusive financial services, shaping the future of finance for both institutions and customers alike. Conclusion The digital revolution is changing the functionality of every other business operating today. Just like all distinct industries that are focusing on leveraging the revolution to increase profits, banking is on the territories as well. The applications and examples present a clear picture of what is in store from the benefit’s point of the use of artificial intelligence in banking. Their focus on scaling new heights in customer relationship improvement through digitization is rising on the progress scale. Although with challenges like cyber threats from cybercrimes, conventional banking methods, lack of training, etc., the world of banking is picturing technology-faced services into the ground level banking operations. If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

by Pavan Vadapalli

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27 Feb 2024

Ethical Hacker Salary India in 2024 [Freshers and Experienced]
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Ethical Hacker Salary India in 2024 [Freshers and Experienced]

Summary: In this article, you will learn about the ethical hacker’s salary in India. Ethical Hacking Job Roles Salary per Annum Ethical Hacker 5.02 LPA to 40 LPA Network Security Engineer 2.47 LPA to 10 LPA Cyber Security Analyst 3 LPA to 10 LPA Penetration Tester 6 LPA to 20 LPA Information Security Manager 16 LPA tp 30 LPA Cyber Security Engineer 2.8 LPA to 20 LPA Security Consultant 3.1 LPA to 20 LPA Read more to know each in detail. We read and hear about cyber-attacks and hacking threats constantly. Do you ever wonder who combats these attacks and protects organisations?  The answer is ethical hackers. Unlike traditional hackers, who breach digital security systems to steal data for extorting money, ethical hackers test out the digital security systems to enhance their strength. Even though the role of ethical hackers has been in the industry for several years, demand for ethical hackers is soaring high right now. That’s because we’ve witnessed a significant increase in cyber-attacks in the past decade. What’s more is the techniques used by cybercriminals have become highly advanced and complex, with new variations of malware constantly making it to the market. Naturally, organisations need ethical hackers to mitigate such threats. While this is quite a unique field, it is also financially lucrative. Today, we’ll discuss the average certified ethical hacker salary in India to understand why now is the perfect time to pursue a career in ethical hacking.  Check out our free courses to get an edge over the competition. If you’ve ever wondered about how secure a public WiFi network is, you’ve probably also wondered about all the ways in which the data that we create on a daily basis is liable to be intercepted. And it’s not just about the data that we create or consume – different types of networks have different connections that are protected in various ways, and what would happen if some of those ways were discovered by people who had malicious intentions? Well, if these thoughts have run their course through your mind, and you’re wondering what you can do about it – well, then ethical hacking is the profession for you! But what if you’ve never heard of the term “ethical hacking” before? It’s entirely possible that you’ve only thought about hacking as the thing that people who steal from banks, or museums, or sensitive data – basically you may have only thought of hacking as something that aids criminals in stealing. Check out upGrad: Advanced Certification in Cyber Security That’s just the popular portrayal of it. In reality, hacking can also be used to find out faults with security measures, test the strength of various networks, and much more! And this is what is referred to as ethical hacking. Who is an Ethical Hacker? An ethical hacker or a white hat hacker, is a skilled computer security professional authorized to penetrate computer systems, networks, and applications to identify vulnerabilities and weaknesses. The primary goal of an ethical hacker is to proactively find and fix security flaws before malicious hackers or cybercriminals can exploit them. Ethical hackers use their expertise to simulate real-world cyberattacks in a controlled and legal environment. They follow a strict code of ethics and obtain permission from the owners of the systems they are testing before conducting any penetration or security assessments. Doing so helps organizations identify and rectify vulnerabilities that could lead to data breaches, financial losses, or other cybersecurity incidents. Ethical hackers typically deeply understand various hacking techniques, programming languages, networking protocols, and operating systems. They use their knowledge to analyze and assess the security posture of systems, networks, and applications, often using the same tools and methods that malicious hackers might employ. However, their intentions are purely to improve security rather than cause harm. In addition to identifying vulnerabilities, ethical hackers provide recommendations and strategies to mitigate risks and enhance overall security. Their work is crucial in the ongoing effort to protect sensitive information, maintain the integrity of digital systems, and ensure the privacy of individuals and organizations in an increasingly interconnected and digital world. What are the responsibilities of an Ethical Hacker? The responsibilities of an ethical hacker encompass a range of tasks aimed at identifying and addressing vulnerabilities in computer systems, networks, and applications. Here are some of the key responsibilities: Security Assessments Conducting comprehensive security assessments of systems, networks, and applications to identify potential vulnerabilities and weaknesses that malicious actors could exploit. Penetration Testing Performing controlled and simulated cyberattacks to determine the effectiveness of an organization’s security measures. This involves attempting to exploit vulnerabilities in a controlled manner to assess their impact. Vulnerability Identification Identifying software and hardware vulnerabilities, misconfigurations, and other security issues that could compromise the confidentiality, integrity, or availability of data and systems. Risk Analysis Evaluating the potential risks associated with identified vulnerabilities and ranking them based on severity and potential impact. This helps organizations prioritize which vulnerabilities to address first. Reporting Documenting findings from security assessments and penetration tests in detailed reports. These reports typically include information about the vulnerabilities discovered, the methods used to exploit them, and recommendations for remediation. Recommendations Providing actionable recommendations for improving security posture. These recommendations might involve suggesting patches, configuration changes, or best practices to mitigate identified vulnerabilities. Collaboration Working closely with IT and security teams to understand the organization’s infrastructure and systems. Ethical hackers may collaborate with other security professionals to implement security solutions and measures. Continuous Learning Stay up-to-date with the latest hacking techniques, cybersecurity trends, and threats. This continuous learning is essential for staying ahead of cybercriminals and adapting to evolving security landscapes. Legal and Ethical Compliance Ensuring that all activities are conducted within the boundaries of legal and ethical frameworks. Ethical hackers must obtain proper authorization before performing any security assessments and adhere to guidelines to protect the organization’s assets and privacy. Training and Awareness Conducting training sessions and awareness programs for employees to educate them about cybersecurity best practices. This helps organizations foster a security-conscious culture. Incident Response Assisting in incident response efforts by providing insights into the nature of cyberattacks and helping to mitigate their impact. Ethical hackers may contribute to the investigation and recovery process. Code Review Analyzing source code and software applications to identify potential security vulnerabilities and weaknesses. This proactive approach helps prevent security issues from being introduced during the development phase. Why Should you Consider Ethical Hacking? There are many compelling reasons to consider ethical hacking, and some of them may even surprise you! For instance, did you know that there are more cyberattacks occurring per second now than ever before in the history of the universe? The most recent estimate for this is somewhere between one attack every 11 to 14 seconds. This means that companies, no matter their size or strength or turnover, are always under threat of losing their precious data, as well as any industry secrets that they may be guarding closely. Needless to say, this has translated into a very specific concern when it comes to evaluating cybersecurity concerns, as well as threat vulnerability, no matter what part of the spectrum is open to being intercepted. Check Out upGrad Advanced Certification in DevOps And there’s a very logical reason behind the now increased responsibility that needs to be shouldered by ethical hackers. Think about it – data is, without doubt, one of the most important things that a company can produce, acquire, or exchange today. Therefore, it can directly impact a company’s bottom line, not to mention the top line. Ethical hackers are therefore in significant demand these days, not only because they can protect the data that companies need on a daily basis, but also because they can provide significant inputs into how to make the extant security systems better and more efficient in terms of guarding against rogue attacks and specific malware. If you like a job where you feel the pressure of performing every single day, then ethical hacking is the right career choice for you! If you’ve already accepted that one of the best ways to get noticed is by setting yourself up in do-or-die situations, then ethical hacking may be the exact fit for you! Read on to find out what kind of careers await you in the wonderful field of ethical hacking. upGrad’s Exclusive Software and Tech Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Why are Ethical Hackers in Demand? Considering the importance placed on ethical hacking as well as everything else that is related to it, it’s worth it to spend some more time trying to understand the demand. It comes from the fact that data breaches are ultimately not a matter of possibility but rather one of temporality. This basically means that data breaches are going to happen sooner rather than later, and it’s in the best interest of every company to keep their data in the hands and minds of people whose entire job consists of protecting this very data. Moreover, businesses that choose to invest in their cybersecurity make sure that they also have an edge over other companies simply because they go the extra mile to ensure that there are no security lapses. When it comes to stakeholders of companies – especially those who are not experts in tech, let alone cybersecurity – they understand how important it can be to maintain a competitive edge over other competitors and therefore understand the importance of this investment as well. Ethical hacking-related roles such as penetration tester, computer security, or web security and encryption can easily make the companies pay INR 5 to 9 LPA. Thus on average, the ethical hacker salary per month in India is around INR 5 LPA. However, the highest salary of ethical hacker in India can rise up to INR 30 LPA as well. Factors such as ethical hacker monthly salary in India can easily waver due to factors like experience and skillset, so make sure to strengthen your resume.  As you read this, companies across the world have widened up to the security requirements of the present age. There are now as many as 3 times the ethical hacker jobs than there are people who can do the work that these jobs entail. This basically means that vacancies are increasing at a steady rate as companies start looking to find people to fill up these roles, while the general demand for these roles increases at an alarming rate.   Some of the other reasons why cybersecurity has come into its own as a business concern is an increasing rate at which cloud computing has been taken up by companies of all sizes and belonging to all industries. This not only puts their entire business data online but also exposes them to security issues that come with inconsistent security updates. In addition to this, security spending has grown at a fast pace, and when coupled with the overall number of internet users, it only means that there is an ever-growing need to be security conscious. This being said, there is yet another factor that ties everything –  every single online threat is increasing in the type of complexity and range of impact. So, every single company out there is looking to diversify their knowledge base when it comes to cybersecurity and understanding how best to deal with the demands of security needs.  Now let’s take a look at some of the career opportunities in the field of ethical hacking in specific and cybersecurity in general. Why is the Ethical Hacker salary in India high? Ethical hackers ensure that a company remains safe from any form of data breaches. They install and incorporate the appropriate safety infrastructure, measures, and technologies to prevent system breach or violation. The looming threat of cybercrimes is ever prevalent today. According to the Government of India, cyber attacks in India rose by 300% in 2020. Be it small businesses or large corporations, government agencies or private companies – nobody is safe from hackers. A single data breach can result in a staggering financial loss for an organisation. When it comes to national security issues, the Indian government has to pay special attention to fortify the cybersecurity infrastructure. Just a few months ago, the data of 2 crores Bigbasket users, a popular grocery delivery platform, was compromised. Similarly, unknown hackers made a ransomware attack on Haldiram and demanded $7,50,000 from the namkeen manufacturer. These stats and instances show how crucial it is for organisations of all shapes and sizes to invest in cybersecurity and hire the right professionals for it. Since cybersecurity is a complex and highly specialised field, the average ethical hacker salary in India is pretty high. Another reason why ethical hackers’ salary is high is that there’s a lack of talent in this domain. Even though there’s a huge demand for ethical hackers and cybersecurity professionals in India, the supply is quite limited. Hence, companies pay handsomely for these roles to attract the best talent.  Many companies have started adopting cloud computing technologies to store their data. While cloud computing solutions promise efficiency, speed, and reduced costs to their users, they make the data more vulnerable to ransomware and malware attacks. This has also caused an uptick in demand for ethical hackers. Global spending on cybersecurity is also expected to exceed $1 trillion by 2022. Most importantly, government associations such as CBI, and ICERT under DeitY (Department of Electronica and Information Technology), recruit cyber security experts. In such a way, one can get a reliable source of income, and for such cases, a mid-level ethical hacker monthly salary in India may range anywhere from 40K to 60K. Hacker jobs in CBI salary can be estimated near this figure as not much is known about it. However, considering the importance of this role, Hacker jobs in CBI salary would be significantly higher than generic hacker jobs.  Ethical Hacking Career Opportunities, Salaries, and Progression The ethical hacker salary in India ranges from INR 1.77 lakh per annum and goes up to INR 40 lakh per annum. Bonus for this role ranges from INR 5,000 to INR 2 lakh, whereas shared profits go up to INR 5.11 lakh.  The certified ethical hacker ranges so vastly because it depends on many factors, including your experience, company reputation, skills, location, company’s pay structure, etc.  Many ethical hackers choose to become self-employed and launch their own business. This is an ideal pathway for those who want to freelance. If you have a knack for entrepreneurship, you can even launch your cybersecurity firm. However, it would be best to gain some experience in the field before you start your business.  Becoming a successful ethical career doesn’t require one and only path – it is a multifaceted role, and therefore has multiple points of entry, as well as unique growth opportunities that can take you in some very different directions in your professional career! No matter whether you’re coming into this role directly from a college degree or if you’ve had some IT experience in a different field, you can still come into this field with the expectation that you will grow and can make a name for yourself by providing the practical knowledge that gets the work done! The salary of ethical hacker largely depends on the skills they have and how knowledgeable they are about the field rather than how many years of practice they have. As per Glassdoor, an ethical hacker salary in Google per month is around INR 5 lakhs! Factors Affecting Ethical Hacker Salary in India In most cases where individuals work freelance, it becomes difficult to estimate the ethical hacker salary per month. The reason why the salary range of ethical hackers in India varies greatly is due to multiple factors. However, out of those numerous factors, the most notable ones are: Your location (the city you work in). Your experience (how many years you have spent in the industry). Your skills (your expertise in the trade). Your employer (which company you work for) Your job role Understanding these factors can help you in determining how much you can expect to earn from this role. Moreover, you can use this information to plan your career more effectively.  In the following points, we’ll discuss these four factors in more detail: Location The city you work in plays a huge role in determining how much you’ll make as an ethical hacker.  Pay for ethical hackers varies from city to city. That’s because the cost of living is different for every city. Living in a metro city would be more expensive than a non-metro city. Moreover, a city’s work environment and culture also influence the average pay offered in that place. Hence, the highest salary of ethical hacker in India can differ in particular places.  Cities that offer the best pay to ethical hackers in India are: Hyderabad (49% more than the average) Delhi (19% more than the average) Kolkata (19% more than the average) Jaipur (50% more than the average) Similarly, some cities offer below-average pay to ethical hackers in India. These cities include: Pune (21% less than the average) Mumbai (21% less than the average) Bangalore (10% less than the average) Experience Another notable factor that affects your ethical hacker salary in India is the amount of experience you have as a professional. Generally, as you gain more experience in the field, the higher your pay will be. Entry-level and early-career ethical hackers earn 2% and 5% less than the average, whereas late-career ethical hackers make 447% more than the average. Ethical hackers with less than a year’s experience earn INR 4.93 lakh per annum on average in India. Those with five to nine years of professional experience in this field make INR 7 lakh per annum on average.  On the other hand, ethical hackers with 10 to 19 years of professional experience earn INR 30 lakh per annum on average. You should keep in mind that you can get promoted to senior roles in your organisation that pay much higher salaries with experience.  Skills Your knowledge of this field and your technical expertise will help you greatly in attracting higher pay as an ethical hacker. Developing the most in-demand skills is the best way to command high-pay as a professional.  You should focus on seeing which skills are popular in this field and which skills offer the best pay. In terms of popularity, the most popular skills among ethical hackers are cybersecurity, Linux, network security management, and IT security & infrastructure.  Alternatively, the most financially beneficial skill for ethical hackers is network management as it offers 59% higher than average pay. Other skills that offer above-average pay to ethical hackers are computer security and IT security & infrastructure – each of them offers 9% higher than average pay.  Company Different organisations offer different salaries to ethical hackers. Companies that provide some of the best salaries to ethical hackers are HCL Technologies and Tata Consultancy Services. The pay of ethical hackers at Tata Consultancy Services ranges from INR 5.17 lakh per annum to INR 5.54 lakh per annum.  You should choose your employer carefully in this field. We recommend doing thorough research on different organisations before selecting a particular company.  Job Roles Ethical hacking is a branch of cybersecurity. There are a plethora of career opportunities in this field. Following are some of the most lucrative job roles you can pursue in this field 1. Ethical Hacker An ethical hacker tests the digital security implementations of an organisation to identify its weaknesses and suggest improvements accordingly. They are experts who can penetrate networks to find security vulnerabilities. Black-hat hackers exploit these vulnerabilities to breach an organisation’s cybersecurity system. Hence, ethical hackers regularly check the company’s network security systems to identify vulnerable areas and fix them. They also train the organisation’s staff about the best security practices and standards.  What is the average ethical hacker salary in India? The average ethical hacking salary in India is INR 5.02 lakh per annum. Pay in this field can go up to INR 40 lakh per annum depending on your experience, skills, and other factors.  2. Network Security Engineer A network security engineer handles and manages the overall technology stack of an organisation. Network security engineers provision, deploy, configure, and administer the various network and security-related hardware and software of a company.  They maintain firewall, web security programs, virtual private networks, etc. They also support the code environment to protect switches, servers, and the organisation’s IT infrastructure.  What is the average network security engineer salary in India? The average network security engineer salary in India is INR 5.34 lakh per annum. Pay for this field starts from INR 2.47 lakh per annum and goes up to INR 10 lakh per annum. Notable skills in this field are security policies and procedures, WAF (Web App Firewall), and IT security & infrastructure. 3. Cyber Security Analyst A cybersecurity analyst is responsible for protecting an organisation from cyber threats. They do so by using various processes and technologies that detect and prevent these threats.  Cybersecurity analysts are responsible for testing their organisation’s security implementations to determine how well they perform against real threats. They work with other cybersecurity professionals to help strengthen their company’s defences against hackers and malware. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript What is the average cybersecurity analyst salary in India? A cybersecurity analyst in India earns an average salary of INR 5.29 lakh per annum. Their pay starts from INR 3 lakh per annum and goes up to INR 10 lakh per year, depending on their experience and skills. 4. Penetration tester: A penetration tester, aka pen tester, is an individual who performs simulated cyberattacks on a company’s computer systems and networks to assure the effectiveness of its firewalls and other cyber security measures. With the help of a pen tester, the companies can identify the potential vulnerabilities and weaknesses before actual hackers try to breach in.  A pen tester is responsible for performing tests on applications, designing and conducting simulated social engineering attacks, researching different types of attacks, reverse engineering malware spam, writing technical and executive reports, and automating the repetitive tests to make the process more efficient, etc. Compared to the ethical hacker salary in Google per month, the penetration tester salary might be a bit lower but, the job role is pretty lucrative to miss out! What is the average pen tester salary in India?  The average penetration tester salary in India is around INR 6 Lakh per annum. However, based on varied reasons, the pay may also rise up to 20 lakh per annum. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 5. Information security manager: An information security manager is responsible for overseeing and controlling a business’s computer-related security aspects. The managerial role demands the individual to plan and carry out security measures to protect the business from any sorts of data breach and malware attacks, or even unauthorised access.  Their responsibilities include upgrading the existing security systems, testing security products, overseeing stimulated breaches and detecting weaknesses, preparing technical documentation, making disaster recovery plans, etc.  What is the average information security manager salary in India? The average salary of an information security manager in India is around INR 16 lakh per annum and can easily escalate up to INR 30 lakh per annum. 6. Cyber security engineer: A cyber security engineer helps a company to design and implement methodologies that secure the company’s cyberspace. The major roles and responsibilities include planning, implementing, monitoring, and also upgrading the security measures, troubleshooting the network bugs, ensuring the protection of the organization’s data and infrastructure, testing and spotting the network and system vulnerabilities, and evaluating the company’s security needs, etc.  Good problem-solving ability and in-depth knowledge of computer science are the two essential skills required for the job.  What is the average cyber security engineer salary in India? A cyber security engineer in India errands an average of INR 6.5 lakhs per annum. However, for various reasons, it may range between 2.8  to 20 lakh per annum. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 7. Security Consultant: Security consultants may work under any agency or provide individual services. In both cases, their job is to engage with the clients, understand their security needs, and provide them with scope solutions that protect their computer networks and digital assets. Security consultants may also design and implement strategies to improve organizational cybersecurity.  What is the average security consultant salary in India? On average, a security consultant in India may earn around INR 8.4 lakhs per annum; however, in varied circumstances, the earnings may range anywhere between 3.1 to 20 lakhs per annum.  Ethical Hacker Salary in Other Countries (UK & USA) Apart from India, the demand for ethical hackers is significantly high all across the globe. Organisations everywhere realise the importance of cybersecurity implementations and want to protect themselves from the dangers of hackers and malware. Ethical Hacker salary in the US The average ethical hacking salary in the US is $93,566. Popular employers for ethical hackers in the US include Booz, Allen and Hamilton, Lockheed Martin Corp, US Army, and Northrop Grumman Corporation.  Ethical Hacker salary in the UK In the UK, the average ethical hacking salary is £44,525 per annum. Prominent employers for ethical hackers in the UK include BT Group, Claranet, Barclays Bank PLC, CGI Group Inc, etc.  What is the future of ethical hacking? The career of ethical hacking and related fields poses an infinite future both in government and corporate fields. Career prospects are expected to only rise exponentially as the use of the internet and related fields keep growing. With the continuous use of social media and the internet, the number of internet crimes is increasing, adding to the demand for ethical hackers and related cyber security roles. Apart from that, the finance industry is also showing a great inclination towards technological advancements, making it inevitable for them to hire cyber security personnel. The surge in people with the knowledge of ethical hacking will also significantly increase the salary of ethical hackers. Even as per the reports of google trends, people’s interest in ethical hacking has grown compared to the last year. Along with that, as per the reports of Cybercrime magazine, there will be approximately 3.5 million global job openings in the year 2025 How to become an Ethical Hacker? By now, it’s clear to you that ethical hackers are the front-line defence of any organization against cyber threats. An ethical hacker must be well-versed with cryptography, computer networks, and the basics of Internet security. If you’re interested in pursuing a career in this field, you should take a cybersecurity course. Taking a cybersecurity course will help you learn all the required concepts through a structured curriculum in a stepwise manner.  At upGrad, we offer the following two cybersecurity courses: Advanced Certificate Programme in Cyber Security  It’s an online program that lasts for 7.5 months only. You will get 300+ hours of study material in this program with all of our exclusive benefits. You will learn about network security in ethical hacking, application security in ethical hacking, and plenty of other related concepts.  Both of these courses allow you to study directly from the industry through live sessions, videos, lectures, and assignments. The projects allow you to test your knowledge of the subjects you learn during these problems and see how much you have learnt.  What are the Different Roles you can get as an Entry-Level Ethical Hacker?  There are many roles that you can get as you start out – and the most require that you’ll have to fulfil is to have general IT knowledge and experience. Once you’ve understood the theoretical bits, you can implement that to understand how to protect knowledge in practice. Here are some of the jobs you can get: Web administrator IT technician Database administrator Security administrator Web developer Now, although these jobs don’t directly have “ethical hacker” or “cybersecurity” in the job description, there is a very definite option to graduate into those roles from these more basic positions. All you’ll need to do is take up a cybersecurity course online – and from there, everything can get sorted for you, in terms of having precedence for a cybersecurity job role! For these entry-level positions, you can earn an average of INR 2.5 lacs per annum, and it can go up to INR 5 per annum as well! From here on out, it gets much more lucrative from a strictly financial perspective. Moreover, the quality of work also gets more engaging and you can end up expressing yourself in a much more satisfying manner as well. Your best bet will be to consider some certifications that will take up specific management issues, thus making your contributions to any role that much more helpful. The Next Step: Ethical Hacking Once you’ve completed the required certifications, you can take a look at ethical hacking as a full-time job option. As an ethical hacker – or a penetration tester, testing how deep the security of the network penetrates into the network – you will be looking at the various weak spots that can exist in a particular network. You will basically be simulating actual cyber-attacks so that you know which part of the network is the most vulnerable and to which part of your attack. The median salary for an ethical hacker is around INR 5 LPA. With up to 3-5 years of experience, it can go up to INR 7.2 LPA as well. The job of an ethical hacker includes penetration testing on all parts of extant networks to make sure that they can handle performance stress tests. They also have to make sure that they can communicate their research to other teams in the same organization so that they can improve upon the network’s faults, especially when it comes to existing security holes. Once they have understood which specific vulnerabilities get triggered through which attacks, their task becomes to make sure that there are newer, more improved tests to make sure that the network can continue to be protected. Once you have spent some time trying to contend with various networks and how well they can hold up under specific stresses of cyber attacks, you can move onwards to make sense of the entirety of the security architecture that encompasses the network all around. The easiest way to reach the next step is to take certain online certifications that will significantly bolster your resume and make you a strong contender for the role of a security architect. And you can read all about it below! The Next Step: Security Architecture Once you’ve figured out the basics of IT security and ethical hacking, the next logical step is to work your way towards understanding security architecture as a whole. Here, you can contend with how larger structures operate and how they impact every little aspect of the way things function within them. Security architecture is largely concerned with how a security network functions within an organization. So, a security architect looks at how the overall structure is designed and how it is built and how it is implemented. Their primary task is to protect against intrusive malware or viruses, or other kinds of attacks. As a security architect, you can earn a significant amount of money when it comes to having a job that involves security architecture. Typically, you’d need at least 7 to 10 years of experience in the security field to reach this position, and once you’ve reached this level of experience, this job will not only make sense but also offer itself a great opportunity for you. The base salary for a security architect is around 19.8 LPA and can go as high up as 30 LPA! So, needless to say, it is a monetarily lucrative position, and it will even satisfy you in terms of what you will have to do on a daily basis.  As a security architect, your job will include designing security systems that can serve across a huge range of IT projects. You will also have to understand what kinds of requirements are relevant when it comes to certain networks or network devices, and undertake risk analysis as well as perform vulnerability testing on all kinds of networks. In addition to this, you’ll also have to implement the best systems and practices for all the networks under your jurisdiction.   The Last Step: Chief Information Security Officer After you’ve worked your way through the earlier levels, you’ll reach a stage when you have a thorough understanding of security networks and processes, how these structures are constructed and how to build them on your own. At this point, your natural tendency will be to want to do more of the same, in as far and wide a range as possible. And this is exactly what the role of the Chief Information Security Officer will allow you to achieve! As the “Chief” title may give away, there is a lot at stake here, and a lot to gain as a result. Let’s take a quick look at what this role consists of, and how you’ll have to wrap your head around it! Well, at a basic level, you’ll have to handle and oversee all of the security initiatives in your company, and make sure that everything is always on point when it comes to tactical protection from threats, as well as to make sure that there are no open vulnerabilities on your part either. This role provides a certain degree of freedom in terms of how you have to operate on a daily basis. As long as you get the results that the company requires, you have full control over how you set up your daily operations and how you delegate work to others. In the role of this responsibility, you will be reporting to either the Chief Information Officer or the Chief Executive Officer. To reach this level, you will already have to have spent over a decade in the IT industry as a security specialist before you can be eligible. The more time that you spend dealing with security teams and overall security ops, the more the dice will be in your favour. The average salary of a CISO is 23 LPA, and it can go as high up as 55 LPA, or even more! As a CISO, your daily job role will involve you in managing IT security levels of all expertise and skill levels. You will also have to work closely with corporate stakeholders to understand their requirements when it comes to the security front, as well as creating plans for utilizing information security technologies to the best of their capabilities. Corporate security will of course be a great part of your job role, you will also have to step in whenever there’s a possible data breach or vulnerability being exposed, and you will need to know exactly which steps to take to make sure that things go well from that point onwards. Overall, this is no doubt going to be a very demanding role – but if you know exactly what you want from your work, and you can do that work well, this will be a very enjoyable role for you! Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Top MNCs Hiring Ethical Hackers in India In the dynamic landscape of cybersecurity, ethical hacking has become integral to safeguarding organizations against cyber threats. If you’re looking for a rewarding career as an ethical hacker in India, several reputable companies offer exciting opportunities. Accenture Overview: Accenture is one of the world’s largest consulting firms, boasting a global presence with offices in over 120 countries. The company specializes in diverse services, including IT, business, and management consulting. Ethical Hacking Role: Within Accenture, ethical hackers are crucial in assisting clients in securing their systems. By identifying vulnerabilities before malicious actors can exploit them, these professionals contribute to robust cybersecurity measures. Wipro Overview: Wipro, a global IT services company, operates in over 50 countries, offering a spectrum of services ranging from software engineering to system integration. Ethical Hacking Role: Wipro’s ethical hacking team is instrumental in fortifying client systems against cyber threats. Their focus is proactively identifying vulnerabilities and ensuring preemptive measures are taken to bolster cybersecurity. IBM Overview: IBM, a technological giant, provides a conducive environment for ethical hackers to thrive. The company boasts a dedicated division, the Security Innovation Lab, fostering skill development. Ethical Hacking Role: Ethical hackers at IBM are actively involved in enhancing their skills through specialized programs. Collaborations with organizations like HireTECH Talent Solutions further open avenues for rewarding career opportunities within the company. Capgemini Overview: Capgemini, a global corporation, offers a range of training programs to nurture ethical hacking skills. The company is recognized for providing comprehensive benefits and ample opportunities for continuous learning. Ethical Hacking Role: Ethical hackers at Capgemini engage in ongoing skill development initiatives. The company’s commitment to training ensures that professionals stay at the forefront of cybersecurity advancements. Amazon Overview: Amazon, a renowned e-commerce and technology company, stands out for its strong reputation in the industry. Ethical Hacking Role: Ethical hackers at Amazon benefit from working in an environment that thrives on innovation and collaboration. The company’s commitment to cybersecurity means that ethical hackers contribute to maintaining the highest standards of security within the organization. Top Skills Needed to Become an Ethical Hacker Planning to make a career as an ethical hacker demands a multifaceted skill set that extends beyond cybersecurity. To navigate the intricacies of ethical hacking effectively, professionals must possess expertise in programming and scripting, networking, hardware knowledge, and a profound understanding of security concepts. Programming and Scripting Proficiency Ethical hackers are not merely users of existing tools; they need the ability to create customized solutions tailored to unique challenges. This necessitates a strong command of programming languages and scripting. Application: From deciphering existing codebases to crafting scripts from scratch, ethical hackers leverage programming skills to develop tools and methodologies that enhance their ability to identify vulnerabilities and mitigate potential threats. Networking Acumen Understanding the intricate workings of networks is paramount for ethical hackers. This encompasses comprehending data flow dynamics, troubleshooting issues that may arise at choke points, and cultivating effective networking skills for collaboration within the cybersecurity community. Application: Ethical hackers delve into the intricacies of network architectures, gaining insights into how data traverses through various nodes. Networking proficiency allows them to decipher potential vulnerabilities and optimize security measures. Hardware Knowledge Mastery A comprehensive grasp of hardware components within a network is indispensable for ethical hackers. This includes routers, switches, and other devices orchestrating the data flow and an understanding of the software running on these machines. Application: Ethical hackers navigate the complexities of hardware configurations, ensuring a holistic understanding of the interconnected systems. This knowledge extends to wireless networks and diverse devices connected via various mediums, empowering ethical hackers to identify potential security loopholes. Security Concepts Expertise Ethical hacking necessitates a profound comprehension of security concepts. This encompasses encryption algorithms (e.g., SHA-1 vs. MD5), hashing algorithms (MD5 vs. SHA-1), and distinctions between symmetric and asymmetric keys (e.g., AES vs. RSA). Application: Ethical hackers leverage their knowledge of security concepts to evaluate and strengthen the resilience of systems against potential threats. This expertise informs their decision-making processes, ensuring robust security protocols that align with industry standards. What are the Tips to Increase Ethical Hacker Salary Potential? Becoming an not only about passion for cybersecurity but also about maximizing your earning potential making way for a salary of ethical hacker in india. To achieve this, consider the following tips encompassing skill development, industry visibility, and strategic career choices including pptions to get ceh salary  or ceh salary in india. Continuous Skill Enhancement Investing in continuous skill enhancement is fundamental to maximizing your ethical hacker salary. Stay abreast of emerging technologies, tools, and programming languages relevant to cybersecurity. Regularly update your knowledge to align with the dynamic nature of cyber threats and countermeasures. Build a Robust Professional Network Networking is a powerful tool in the cybersecurity realm. Attend industry conferences, engage with professionals on social platforms, and participate in cybersecurity forums. A strong professional network can open doors to lucrative opportunities, including high-paying ethical hacking roles including ones to have good government hacker salary in India. Gain Specialized Expertise Diversify and specialize your skill set to cater to niche areas within ethical hacking. Specialized expertise, such as penetration testing, mobile security, or cloud security, can make you stand out in the job market, commanding higher salaries for your specialized knowledge. Showcase Practical Experience Employers highly value practical experience in ethical hacking. Beyond certifications, actively engage in real-world projects, contribute to open-source security tools, or participate in bug bounty programs. Practical experience demonstrates your ability to apply theoretical knowledge and can significantly impact salary negotiations. Stay Informed on Market Trends Keep a vigilant eye on market trends, especially regarding the demand for specific ethical hacking skills. Align your skill development efforts with industry needs, positioning yourself as a valuable asset in high-demand areas. Being proactive in adapting to market trends ensures your skills remain relevant and sought after. Pursue Advanced Certifications Acquiring industry-recognized certifications validates your expertise and significantly impacts your earning potential. Certifications such as Certified Ethical Hacker, Certified Information Systems Security Professional (CISSP), and Offensive Security Certified Professional (OSCP) are highly esteemed in the field and can enhance your marketability. Conclusion If you’ve read through this entire article then there’s a really high chance that the security bug has bitten you and you want to do your best to make sure that all your security systems are on the correct track. And why not, what good is an ethical hacker if they can’t secure their own systems? But other than that, you’ve also understood that ethical hacking is a very lucrative field, with a lot of growth opportunities – both in terms of work and in terms of money. So get started on your path to becoming an ethical hacker today, and don’t look back! The quicker you start, the sooner you’ll reach the zenith of your career! If you want to pursue this profession, upGrad and IIIT-B can help you with an Advanced Certificate Programme in Cyber Security. The course offers specialization in application security, cryptography, data secrecy, and network security.

by Pavan Vadapalli

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19 Feb 2024

Top 15 Python AI &#038; Machine Learning Open Source Projects
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36086

Top 15 Python AI & Machine Learning Open Source Projects

Machine learning and artificial intelligence are some of the most advanced topics to learn. So you must employ the best learning methods to make sure you study them effectively and efficiently.  There are many programming languages you can use in AI and ML implementations, and one of the most popular ones among them is Python. In this article, we’re discussing multiple AI projects in Python, which you should be familiar with if you want to become a professional in this field.  All of the Python projects we’ve discussed here are open source with broad audiences and users. Being familiar with these projects will help you in learning AI and ML better. I hope you will learn a lot while working on these python projects. If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIM-K’s Professional Certificate Program in Data Science for Business Decision Making and upskill yourself for the future. Join the Machine Learning Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Python ML & AI Open Source Projects 1. TensorFlow TensorFlow tops the list of open-source AI projects in Python. It is a product of Google and helps developers in creating and training machine learning models. The engineers and researchers working in Google’s Brain Team created TensorFlow to help them in performing research on machine learning. TensorFlow enabled them to convert prototypes into working products quickly and efficiently.  With TensorFlow, you can work on your machine learning projects remotely in the cloud, in the browser, or use it in on-premises applications. TensorFlow has thousands of users worldwide, as it is the go-to solution for any AI professional.  Abstraction is the greatest benefit that TensorFlow offers for machine learning advancement. It helps you to work remotely in the browser, in the cloud, or in on-premises applications. It provides various workflows with in-built, high-level APIs that enable beginners and professionals to develop ML models in different languages. Models developed using TensorFlow can be implemented on platforms such as the cloud, servers, browsers, mobile, edge devices, and more. TensorFlow is a cross-platform framework that works on a broad range of hardware, including CPUs, GPUs, mobile, and embedded platforms. Furthermore, you can TensorFlow AI mini projects with source code on Google’s proprietary TPU (TensorFlow Processing Unit) hardware to accelerate the growth of deep learning models. It is one of those AI python projects with source code that can train and implement deep neural networks for visual recognition, handwritten digit classification, recurrent neural networks, word embeddings, natural language processing (NLP), sequence-to-sequence models for machine translation, and PDE-based simulations. Also read: Excel online course free! 2. Keras Keras is an accessible API for neural networks. It is based in Python, and you can run it on CNTK, TensorFlow as well as Theano. It is written in Python and follows best practices to reduce the cognitive load. It makes working on deep learning projects more efficient.  The error message feature helps developers in identifying any mistakes and fixing them. As you can run it on top of TensorFlow, you get the benefit of the flexible and versatile application too. This means you can run Keras in your browser, on Android or iOS through TF Lite, as well as through their web API. If you want to work on deep learning projects, you must be familiar with Keras.  Imagine that you need a deep learning framework that facilitates rapid prototyping, works efficiently on CPUs and GPUs, and supports convolutional and recurrent networks. Keras is the perfect library for implementing open-source AI projects fulfilling these needs. Keras doesn’t deal with simple low-level operations, unlike independent open-source AI projects. It uses libraries from related deep learning frameworks such as Theano or Tensorflow as backend engines to perform all low-level computations (like convolutions, tensor products, and many more). Keras is one of those AI mini projects with source code that provides easy and rapid backend access. This is because it boasts ready-to-use interfaces. No need to commit to a specific framework because you can rapidly transit back and forth between the several backends. Keras also provides a high-level API that looks after developing models, stating layers, and configuring different models. This API’s loss and optimizer functions help you to develop models; the API’s fit function helps you to train the process. Read: Machine Learning Projects for Beginners 3. Theano Theano lets you optimize, evaluate, and define mathematical expressions that involve multi-dimensional arrays. It is a Python library and has many features that make it a must-have for any machine learning professional.  It is optimized for stability and speed and can generate dynamic C code to evaluate expressions quickly. Theano allows you to use NumPy.ndarray in its functions as well, so you get to use the capabilities of NumPy effectively.  Theano expresses computations using a NumPy -Esque syntax and runs efficiently on CPU or GPU architectures. It is an open-source project developed by the MILA (Montreal Institute for Learning Algorithms) at the Université de Montréal. It is a fundamental library for working on Deep Learning projects and wrapper libraries in Python. Alternatively, it works as a compiler for performing mathematical expressions in Python. It accepts your data structures and transforms them into efficient code. The resultant code uses NumPy, native code (C++), and efficient native libraries like BLAS. All these components help the code run as quickly as possible on GPUs and CPUs. It uses clever code optimizations to obtain the maximum possible performance from the hardware. It becomes easy to deal with this AI project with source code if you know the fundamentals of mathematical optimizations in Python code. Moreover, Theano offers detailed installation instructions for major operating systems like Windows, Linux, and OS X. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 4. Scikit-learn Scikit-learn is a Python-based library of tools you can use for data analysis and data mining. You can reuse it in numerous contexts. It has excellent accessibility, so using it is quite easy as well. Its developers have built it on top of matplotlib, NumPy, and SciPy.  Some tasks for which you can use Scikit-learn include Clustering, Regression, Classification, Model Selection, Preprocessing, and Dimensionality Reduction. To become a proper AI professional, you must be able to use this library.  Check out The Trending Python Tutorial Concepts in 2024 5. Chainer Chainer is a Python-based framework for working on neural networks. It supports multiple network architectures, including recurrent nets, convnets, recursive nets, and feed-forward nets. Apart from that, it allows CUDA computation so you can use a GPU with very few lines of code.  You can run Chainer on many GPUs too if required. A significant advantage of Chainer is it makes debugging the code very easy, so you won’t have to put much effort in that regard. On Github, Chainer has more than 12,000 commits, so you can understand how popular it is.  Chainer is an (open source) deep learning framework written using CuPy and NumPy Python libraries. Japanese venture company “Preferred Networks” in collaboration with Microsoft, Intel, IBM, and Nvidia manages its development. Chainer is flexible and intuitive. You need exclusively designed operations if the network contains complex control flows like loops and conditionals, in the define-and-run approach. But in this approach, the programming language’s native constructs, like for loops and if statements can be utilized to designate such flow. So, Chainer’s flexibility is useful for executing recurrent neural networks. Another benefit of this AI project with source code is the simplicity of debugging. Usually, it is challenging to determine the fault an error occurs in the training calculation when using the define-and-run approach. This is because the code written to define the actual position of the error and the network are separated. If you are searching for Python AI source code for various projects, Chainer should be on your list. 6. Caffe Caffe is a product of Berkeley AI Research and is a deep learning framework that focuses on modularity, speed, and expression. It is among the most popular open-source AI projects in Python.  It has excellent architecture and speed as it can process more than 60 million images in a day. Moreover, it has a thriving community of developers who are using it for industrial applications, academic research, multimedia, and many other domains.  7. Gensim Gensim is an open-source Python library that can analyse plain-text files for understanding their semantic structure, retrieve files that are semantically similar to that one, and perform many other tasks.  It is scalable and platform-independent, like many of the Python libraries and frameworks we have discussed in this article. If you plan on using your knowledge of artificial intelligence to work on NLP (Natural Language Processing) projects, then you should study this library for sure.  Gensim stands for Generate Similar. It is a python-based open-source framework for natural language processing and unsupervised topic modeling. It extracts semantic concepts from documents. It can also manage extensive text collections. So, it differentiates itself from other ML software packages that use memory processing. It is one of the best AI projects for beginners with source code that improves processing speed. This is because it offers efficient multicore implementations for different algorithms. It features more text processing features than many other packages like R, Scikit-learn, etc. It uses the best models and modern statistical machine learning (like Creating word or document vectors) to perform various complex tasks. It also detects semantic structure in plain-text materials. Gensim is one of the popular AI projects for beginners with source code because it has been used in many applications including Word2vec, fastText, Latent Semantic Analysis (LSA),  Latent Dirichlet Allocation (LDA), and Term Frequency-Inverse Document Frequency (TF-IDF). Our learners also read: Top Python Free Courses Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 8. PyTorch PyTorch helps in facilitating research prototyping so you can deploy products faster. It allows you to transition between graph modes through TorchScript and provides distributed training you can scale. PyTorch is available on multiple cloud platforms as well and has numerous libraries and tools in its ecosystem that support NLP, computer vision, and many other solutions. To perform advanced AI implementations, you’ll have to become familiar with PyTorch. PyTorch started its journey as a Python-based substitute for the Lua Torch framework. Initially, it focused only on research applications. Presently, the PyTorch ecosystem includes tools, projects, libraries, and models developed by a community of industrial and academic researchers, deep learning experts, and application developers. PyTorch is better than other AI python projects with source code. This is because it uses dynamic computing that offers excellent flexibility in developing complex networks. Moreover, PyTorch features a readable syntax, so users can easily grasp it. PyTorch improves the AI models’ optimization with the help of Python’s intrinsic potential for asynchronous implementation. Its Distributed Data Parallelism feature facilitates project development by running models across various computers. PyTorch’s capability to construct ML/DL solutions is vast because the community behind it expands. If you are looking for artificial intelligence projects with source code in Python, you can check out Pytorch. Read more: Tensorflow vs Pytorch – Comparison, Features & Applications Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 9. Shogun Shogun is a machine learning library (open-source) and provides many unified as well as efficient ML methods. It is not based on Python exclusively so you can use it with several other languages too such as Lua, C#, Java, R, and Ruby. It allows combining of multiple algorithm classes, data representations, and tools so you can prototype data pipelines quickly.  It has a fantastic infrastructure for testing that you can use on various OS setups. It has several exclusive algorithms as well, including Krylov methods and Multiple Kernel Learning, so learning about Shogun will surely help you in mastering AI and machine learning.  Shogun provides various unified and proficient machine learning algorithms. This project’s core is kernel machines like support vector machines for solving classification and regression problems. It is one of the versatile AI projects with source code because its scope is not limited to any single language. You can use its toolkit across a unified interface (SWIG) from Python, C++, C#, Octave, Java, R, Ruby, Lua, etc. Its ML toolkit inspires the development journey with its key characteristics like open source and accessibility. Shogun is popular for being one of the oldest and biggest open-source ML platforms.  Anyone can effortlessly learn it by simply connecting to a Jupyter Notebook. It is possible to run it in Cloud.  All the standard ML algorithms’ implementation is competitive according to the MLPack benchmarking framework benchmark. Shogun is one of the AI projects with source code that offers extensive testing infrastructure. So, it is compatible with various operating systems. If you are looking for Python machine learning projects that can help you train ML models more effectively, you should check Shogun out. 10. Pylearn2 Based on Theano, Pylearn2 is among the most prevalent machine learning libraries among Python developers. You can use mathematical expressions to write its plugins while Theano takes care of their stabilization and optimization. On Github, Pylearn2 has more than 7k commits, and they are still growing, which shows its popularity among ML developers. Pylearn2 focuses on flexibility and provides a wide variety of features, including an interface for media (images, vectors, etc.) and cross-platform implementations.  11. Nilearn Nilearn helps in Neuroimaging data and is a popular Python module. It uses scikit-learn (which we’ve discussed earlier) to perform various statistical actions such as decoding, modeling, connectivity analysis, and classification. Neuro-imaging is a prominent area in the medical sector and can help in solving multiple issues such as better diagnosis with higher accuracy. If you’re interested in using AI in the medical field, then this is the place to start.  Read: Scikit-learn in Python: Features, Prerequisites, Pros & Cons 12. Numenta Numenta is based on a neocortex theory called HTM (Hierarchical Temporal Memory). Many people have developed solutions based on HTM and the software. However, there’s a lot of work going on in this project. HTM is a machine intelligence framework that’s based on neuroscience.  13. PyMC PyMC uses Bayesian statistical models with algorithms such as the Markov chain. It is a Python module and because of its flexibility, finds applications in many areas. It uses NumPy for numeric problems and has a dedicated module for Gaussian processes.  It can create summaries, perform diagnostics, and embed MCMC loops in big programs; you can save traces as plain text, MySQL databases, as well as Python pickles. It is undoubtedly a great tool for any artificial intelligence professional.  14. DEAP DEAP is an evolutionary computation framework for testing ideas and prototyping. You can work on genetic algorithms with any kind of representation as well as perform genetic programming through prefix trees.  DEAP has evolution strategies, checkpoints that take snapshots, and a benchmarks module for storing standard test functions. It works amazingly well with SCOOP, multiprocessing, and other parallelization solutions.  15. Annoy Annoy stands for Approximate Nearest Neighbors Oh Yeah, yes, that’s the exact name of this C++ library, which also has Python bindings. It helps you perform nearest neighbor searches while using static files as indexes. WIth Annoy, you can share an index across different processes so you wouldn’t have to build multiple indexes for each method.  Its creator is Erik Bernhaardsson, and it finds applications in many prominent areas, for example, Spotify uses Annoy for making better recommendations to its users.  Also Read: Python Projects for Beginners 16. XGBoost (eXtreme Gradient Boosting) XGBoost stands out as a prominent open-source machine learning library for Python, gaining widespread popularity in the AI community. Originally developed by Tianqi Chen, this library specializes in gradient boosting, a technique that builds multiple decision trees sequentially to improve model performance. XGBoost excels in solving classification and regression problems efficiently. One of the key strengths of XGBoost lies in its optimization for performance and speed. It implements parallel computing and tree-pruning techniques, making it highly scalable for large datasets. The library also supports various objective functions and evaluation criteria, allowing users to tailor their models to specific tasks. XGBoost has found applications in diverse domains, such as finance, healthcare, and natural language processing. Its adaptability, feature importance visualization, and ability to handle missing data make it an indispensable tool for machine learning practitioners. As an open-source project, XGBoost fosters collaboration and continuous improvement within the machine learning community. Developers and data scientists appreciate its versatility and efficiency in boosting model accuracy across a wide array of tasks. 17. LightGBM LightGBM, a paradigm of excellence in the open-source machine learning landscape for Python, stands as a robust gradient-boosting framework that has garnered accolades for its efficiency and scalability. Conceived by Microsoft, LightGBM is meticulously crafted for distributed and efficient training of machine learning models. Its exceptional speed in model training, coupled with minimal memory usage, positions it as a go-to solution for handling large datasets and achieving state-of-the-art performance in classification, regression, and ranking tasks. LightGBM’s parallel computing capabilities and advanced tree pruning techniques make it a versatile tool embraced by data scientists and researchers across diverse domains. 18. CatBoost CatBoost is the epitome of innovation in open-source machine learning and it distinguishes itself as a high-performance library designed to tackle challenges related to categorical feature support. Born out of the ingenuity of Yandex, CatBoost alleviates the complexities associated with handling categorical features in machine learning. Its unique algorithms empower developers to construct accurate models with minimal preprocessing, making it a valuable asset in the Python ecosystem. CatBoost’s commitment to simplicity, efficiency, and user-friendly design positions it as an indispensable tool for both seasoned practitioners and those entering the realm of machine learning. If you were searching for an artificial intelligence project GitHub, Catboost might be the right one for you. 19. FastAI FastAI is a beacon of accessibility in open-source deep learning and it emerges as a high-level library built on the robust PyTorch framework. Developed by the FastAI team, this library aims to democratize AI, making cutting-edge techniques accessible to both novices and experienced practitioners. FastAI stands out with its user-friendly interface, comprehensive documentation, and a commitment to providing best practices and abstractions. It not only simplifies the complex landscape of deep learning but also empowers users to achieve state-of-the-art results with minimal coding effort. FastAI’s active community and focus on education further contribute to its prominence in the Python AI ecosystem. 20. Hugging Face Transformers Hugging Face Transformers has a towering presence in open-source Python libraries and takes center stage with its transformative impact on natural language processing (NLP). Developed by Hugging Face, this library serves as a treasure trove, offering a rich collection of pre-trained models and tools for a plethora of NLP tasks, including text classification, language translation, and sentiment analysis. The Transformers library not only simplifies the implementation of state-of-the-art NLP models but also fosters a collaborative spirit with an expansive model repository. It has become the cornerstone of the NLP community, guiding researchers and developers through the intricacies of language-related AI projects. 21. scikit-image Scikit-image is undoubtedly an architectural marvel in the realm of open-source image processing for Python and extends the capabilities of scikit-learn into the captivating domain of image analysis. Nurtured by a vibrant and active community, scikit-image exemplifies comprehensive algorithmic prowess, addressing tasks such as image segmentation, feature extraction, and filtering. Its modular design and seamless integration with other scientific computing libraries make it a versatile and indispensable tool for researchers and practitioners immersed in the world of computer vision. scikit-image’s unwavering commitment to simplicity, interoperability, and continuous improvement solidifies its position as a cornerstone in the arsenal of tools for image-related applications within the Python ecosystem. Scikit-learn is one of the most useful machine learning examples Python out there. If you are looking for AI mini projects with source code, scikit-image can help you make the most out of Scikit-learn. 22. Prophet Prophet, an open-source forecasting tool developed by Facebook, has carved a niche for itself in the Python ecosystem. It specializes in time series forecasting, making it an invaluable asset for analysts and data scientists. Prophet is designed for simplicity and ease of use, allowing users to produce accurate forecasts with minimal effort. Its intuitive interface, automatic handling of missing data, and the ability to incorporate holidays and special events make it a go-to solution for predicting trends and patterns in time series data. This is a great example of machine learning mini projects with source code. 23. AllenNLP AllenNLP is a cutting-edge open-source library for natural language processing (NLP) that is engineered to facilitate research and experimentation in the field. Developed by the Allen Institute for Artificial Intelligence, AllenNLP empowers researchers to design and implement state-of-the-art NLP models. Its modular architecture and extensive pre-built components facilitate the development of custom models, making it a playground for innovation in tasks such as text classification, named entity recognition, and language modeling. If you are interested in a Python AI projects with source code, AllenNLP can be right one for you. 24. Optuna Optuna, a hyperparameter optimization framework, emerges as a crucial tool for tuning machine learning model parameters efficiently. Developed by the team at Preferred Networks, Optuna automates the process of finding the optimal set of hyperparameters for a given machine learning model. Its flexibility, support for various optimization algorithms, and seamless integration with popular machine learning frameworks make it a sought-after choice for practitioners striving to enhance model performance through systematic hyperparameter tuning. If you are looking for optimization machine learning projects using Python that is easy to use, Optuna is the right one for you. 25. SpaCy SpaCy is a lightning-fast open-source library for natural language processing and has become a staple in the toolkit of NLP enthusiasts. Developed with efficiency in mind, SpaCy excels in tasks such as tokenization, part-of-speech tagging, and named entity recognition. Its focus on performance and user-friendly design makes it a preferred choice for building robust and scalable NLP pipelines. SpaCy’s pre-trained models, continuous updates, and support for multiple languages contribute to its widespread adoption in both research and industry applications. If you are searching for AI projects with source code, you can check out this NLP project. 26. Bokeh Bokeh can be described as an interactive visualization library, stands out in the Python ecosystem for creating captivating and interactive visualizations for web applications. Developed by Anaconda, Inc., Bokeh supports a variety of plot types, ranging from simple line charts to complex, interactive dashboards. Its seamless integration with Jupyter notebooks and ability to handle large datasets make it an excellent choice for data scientists and engineers seeking to communicate insights effectively through visually appealing and interactive plots. Bokeh’s commitment to open-source principles and active community support solidify its position as a go-to library for creating engaging data visualizations in Python. 27. AllenSDK AllenSDK, an initiative by the Allen Institute for Brain Science, provides a comprehensive set of tools and resources for neuroscientists and researchers. This open-source Python library enables the analysis and exploration of large-scale neuroscientific datasets. AllenSDK facilitates tasks such as accessing brain atlases, retrieving electrophysiological data, and performing complex analyses, making it an essential resource for advancing our understanding of the brain’s intricacies. 28. Surprise Surprise is an open-source Python library designed for building and analyzing recommender systems. It offers a collection of collaborative filtering algorithms, making it a valuable asset for developers and data scientists working on recommendation engines. Surprise’s user-friendly API simplifies the process of experimenting with different recommendation algorithms and evaluating their performance, making it an ideal choice for those delving into the realm of personalized content recommendations. If you are looking for machine learning projects Python, you can check out Surprise. 29. BioPython BioPython stands as a testament to open-source innovation in the realm of computational biology and bioinformatics. This Python library provides tools for working with biological data, including DNA sequences, protein structures, and phylogenetic trees. BioPython’s rich functionality and modular design make it a versatile toolkit for researchers and scientists engaged in biological data analysis and computational biology projects. 30. Plotly Plotly, a dynamic open-source graphing library has become synonymous with creating interactive and visually appealing plots in Python. Whether it’s static charts, 3D visualizations, or interactive dashboards, Plotly excels in conveying complex data insights with elegance. Developed by Plotly Technologies, this library supports integration with popular data science tools and frameworks, making it a preferred choice for professionals seeking to elevate their data visualization game. Plotly’s versatility and active community contribute to its prominence in the ever-evolving landscape of interactive data visualization in Python. Learn More about Python in AI and ML We hope you found this list of AI projects in Python helpful. Learning about these projects will help you in becoming a seasoned AI professional. Whether you begin with TensorFlow or DEAP, it’d be a significant step in this journey. If you’re interested in learning more about artificial intelligence, then we recommend heading to our blog. There, you’ll find plenty of detailed and valuable resources. Moreover, you can get an AI course and get a more individualized learning experience. Python has an active community that most developers create libraries for their own purposes and later release it to the public for their benefit. Here are some of the common machine learning libraries used by Python developers. If you want to update your data science skills, check out IIIT-B’s Executive PG Programme in Data Science program.

by Pavan Vadapalli

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19 Feb 2024

Cyber Security Salary in India: For Freshers &#038; Experienced [2024]
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904916

Cyber Security Salary in India: For Freshers & Experienced [2024]

Summary: In this article, you will learn about cyber security salaries in India. Take a glimpse below. Wondering what is the range of Cyber Security salary in India? Cyber security career in India is witnessing new heights as the days pass by and with the growing popularity and the internet becoming a necessity, the field is only expected to go up the graph. However, the field is still experiencing a labour shortage which makes entry-level cyber security jobs salary much better than the majority of the tech field jobs.  With the increasing adoption of the Internet and online practices in every sphere, Cyber Security has generated a continuously growing concern for all businesses. One statistic reveals that in 2022, one new organization will fall victim to ransomware every 11 seconds. A report from KSN (Kaspersky Security Network) shows that India has experienced a 37% rise in cyber-attacks in the first quarter of 2020. Authorized institutions started to provide the best courses in cybersecurity in India.  Between November 2021 and October 2022, around 15.37% of computers used by global internet users faced at least one malware class attack. During this period, Kaspersky stopped over 500 million attacks originating from online sources worldwide. This emphasizes the ongoing threat of cyberattacks while showcasing the importance of cybersecurity in preventing potential security breaches. According to a report from PricewaterhouseCoopers, India’s cybersecurity market is predicted to grow from USD 1.97 billion in 2019 to USD 3.05 billion by 2022, with a rate one and a half times than the global rate. Also, Check out our free courses Cyber security is crucial in shielding systems and data from ever-evolving threats like malware and data breaches. The increasing interconnectedness of devices amplifies vulnerabilities, emphasizing the necessity for robust protection measures. This demand for cyber security experts stems from a shortage of skilled professionals and the rising frequency of cyber attacks.  Cyber security jobs salary in India is consequently higher due to this scarcity, especially for specialists in areas like ethical hacking and incident response, as organizations prioritize fortifying their defenses against evolving threats. We are sure, this must-have sparked a will to become a cyber security expert within you! Let’s take a look at who exactly is cyber security and what is a typical cyber security engineer salary in India? At university or university at home, choice is yours ! What is Cyber Security? While physical security protects facilities and objects in the real world, cybersecurity protects information systems and data. Cybersecurity is the practice of safeguarding servers, computers, electronic systems, networks, mobile devices, and data from malicious electronic attacks. It’s aimed to protect assets from malicious codes and logins and is applied in a broad range of contexts, from mobile computing to businesses. Cybersecurity ensures and maintains the confidentiality, integrity, and availability of information and data. It protects computers, computer systems, mobile devices, networks, applications, and data against cyberattacks. These attacks interrupt usual business actions, terminate information, and blackmail or ransom. Cybersecurity uses multiple layers to cover the chosen entry points. For example, connected networks, data, and physical devices must be covered. The growing need for cybersecurity professionals leads to an increasing cyber security salary. Check out upGrad’s Advanced Certification in Cyber Security – IIITB Need for Cybersecurity: Various types of attacks in cybersecurity must be curbed. Most individuals can access multiple electronic gadgets; the default one is smartphones. The number of devices surpasses the number of people. So, it increases the targets for attackers. The cyber threat may lead to identity theft, blackmail, the loss of years of memories in digital media, and monetary loss because your devices become useless. The growing need for cybersecurity specialists suggests that cyber security salary per month in India is admirable to deserving candidates. Attacks on large-scale organizations are more exposed; however, small companies are also targeted. Data breaches in large-scale corporations render millions of personal records and this can lead to more attacks. For example, attacks on hospitals might lead to fatalities because more equipment is connected to the internet. Similar is the case with power plants, wherein the outage show rippling effects over multiple industries. Society is completely dependent on a flawless functioning and secure infrastructure. So, it is worth offering a decent cyber security salary to skilled cybersecurity professionals. Cybersecurity is vital in any organization, irrespective of its size. The information is transforming digital through wireless communication networks owing to cutting-edge technology and software across different sectors like educational institutions, government agencies, hospitals, etc. One of the prime functions of cybersecurity is to secure an organization’s sensitive data that can damage its reputation. Cybersecurity deters cyber attackers who target small and large companies and access their essential information and documents. Leading companies are willing to pay high cyber security salary in India to experienced and skilled candidates. Common Categories of Cyber Security Cybersecurity can be divided into the following common categories. Types of Cyber Security      Application Security – Focuses on finding and fixing vulnerabilities in application codes to make apps more secure. This mainly involves using secure coding practices, regular code reviews, and implementing security controls to protect against threats like SQL injection, cross-site scripting (XSS), and other attacks aimed at compromising the integrity or availability of an application. Network Security – Aims to prevent and protect corporate networks from intrusions such as malware or targeted attackers. It is mainly carried out by deploying firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), virtual private networks (VPNs), and other tools and protocols to secure the network perimeter and internal network segments. Operational Security – Includes the processes and decisions for protecting and managing data assets. It encompasses user permissions for network access and the procedures that dictate the way data should be stored and shared safely. Access controls are critical in determining who has authorization to access specific resources within the network. Furthermore, defining and enforcing procedures for secure data handling, encryption methods, and access management are integral to operational security. This practice also involves ongoing risk assessment and mitigation efforts, such as conducting regular security audits and deploying security patches to counter potential vulnerabilities. Information Security – Safeguards data integrity and privacy during its storage and transmission from one machine to another. Data integrity assurance involves measures to prevent unauthorized alterations or corruption of stored information. Data privacy protection is another key facet, ensuring that sensitive information remains shielded from unauthorized access or disclosure. Employing extensive encryption methods and using secure transmission protocols like SSL/TLS are crucial in keeping data safe while it is in transit between different machines or systems.  End-User Education – Anyone can unintentionally introduce a cybersecurity threat such as a virus by failing to follow safe security practices. Thus, educating end-users to delete suspicious emails, refrain from plugging in unidentified USB drives, and other essential lessons are vital for protecting corporate security. By raising awareness and fostering a security-conscious culture, organizations can significantly reduce the likelihood of security breaches caused by human error. Disaster Recovery and Business Continuity – It defines how an organization responds to a cybersecurity incident that causes data loss or service outage and how it will restore its information and operations to return to the same operating capacity as before the event. Business continuity is the plan a company will rely on while operating without specific resources. These plans are essential in mitigating the impact of cyber incidents, minimizing downtime, and ensuring the organization’s resilience in the face of adversity. Endpoint Security –Remote access is a vital component of business models and workflows. But endpoint security guarantees that it doesn’t turn into a liability. Endpoint devices are easier to target, specifically if the users are not warned of the risks and essential cautionary steps. Endpoint security measures, including robust antivirus software, firewalls, regular software updates, and user awareness training about potential risks, play a pivotal role in fortifying these vulnerable access points and preventing unauthorized access or malware infections. Identity Management –The process of issuing rights and system access must be well-documented and closely monitored. All users’ access levels must be monitored and logged through 2-step security measures to avoid impersonator access. Logging and tracking user activities through two-step security measures enable organizations to identify and address potential security breaches effectively, ensuring the integrity and security of sensitive data and resources. Infrastructure Security –It entails the physical facets of computer infrastructure. For example, it includes a controlled power delivery system, strong physical security, fire extinguishers, etc. Cybersecurity professionals skilled in infrastructure security can earn higher cyber security salary in India. They play a critical role in designing and implementing these measures, safeguarding the integrity and availability of an organization’s IT infrastructure.  Read: Career in Cyber Security Types of Cyber Security Threats Cybersecurity faces 3-fold threats Cybercrime – Consists of groups or single actors targeting systems to cause disruption. It covers a broad range of illicit activities like hacking, malware distribution, phishing, identity theft, financial fraud, and ransomware attacks. The motives behind cybercrime often revolve around financial gain, theft of sensitive data, or simply causing chaos and disruption. Cyber-attack – Involves politically motivated information gathering. Political motives mainly drive these attacks, aiming to obtain sensitive information or intelligence from government agencies, organizations, or entities related to political or national security matters. These attacks may target confidential documents, diplomatic communications, or other classified information for espionage, geopolitical advantage, or influencing political decisions. Cyber-terrorism – Aims to cause panic or fear by undermining electronic systems.Unlike cybercrime, cyber-terrorism’s primary objective is not financial gain but to induce terror or cause societal disruption. These attacks target vital systems such as power grids, transportation networks, financial institutions, or communication systems, aiming to cripple infrastructure and undermine public confidence in government or societal institutions. The goal is to create chaos, fear, and instability among the population. Following are some of the methods used to compromise cybersecurity. Malware – Malware is the most common type of cyber-attack, in which a hacker uses malicious software to disrupt a legitimate user’s computer. Malware is often spread through a legitimate-looking download or an unsolicited email attachment. They are intended to make money or can also have a political motivation. There are various types of malware, such as Trojan, virus, ransomware, spyware, botnets, and adware. Phishing – In phishing, a cybercriminal sends people emails that seem to be from a legitimate company to ask for sensitive information such as credit card details or personal data. It is then used to dupe them for financial gain. SQL Injection – In this type of cyber-attack, a cybercriminal exploits a vulnerability in data-driven applications to insert malicious code into a database using a malicious SQL statement for taking control of the database to steal data. Denial-of-service attack – In this type of attack, a cybercriminal prevents a computer from fulfilling legitimate requests. It overwhelms the networks and servers with traffic and renders the system unusable. It prevents an organization from managing its vital functions. Man-in-the-middle attack – This type of attack involves a cybercriminal intercepting communication between two individuals for stealing data. Attack on data availability: Attacks in data availability guarantee that both you and the end-user can’t access your data. It may be entirely malicious and simply an attempt to disrupt business fully or perhaps ask for a fee to give up access. Many organizations have paid cyber attackers to regain access to their data. You can earn a great cyber security salary per month in India if you can mitigate an attack on data availability. Attack on data integrity -Integrity can be of data or organization. For example, the data integrity is compromised if a scoundrel student hacks the school’s database and modifies all the grades. The organization’s integrity is severely compromised if a cyber attacker obtains access to an organization’s database and all the records are leaked online. This is more perceptible to people whose data is stored in the organization’s system. A justifiable cyber security analyst salary is guaranteed if you can mitigate such cyberattacks. Attack on Data Confidentiality -Personal information must be private. If anybody, either internally or externally obtains illicit access to your information, its confidentiality is compromised. This usually happens on an individual level, at homes and workplaces. Mitigation of Cybersecurity Attacks: Understanding various types of data breaches helps implement the required protective measures. The following steps help mitigate cybersecurity attacks. VA and Pen-tests: Organizations must plan regular penetration tests and vulnerability assessments to check infrastructure security. Any loopholes must be identified and patched.  2. DR & BCP: Every organization must employ business continuity plans (BCP) and disaster recovery (DR). It must contain switch-over dry runs to guarantee that the workforces are always ready to efficiently start the security operations from the primary site to the DR site. Also, it should be ensured that the organization would begin working instantly. Professionals skilled at mitigating these types of cyberattacks can have a decent cyber security salary per month. 3. User Education and Training: Employees must be educated on vigilance’s significance in the context of cybersecurity. Social engineering and phishing must be properly illustrated. 4. Cloud Security: With the increasing adoption of Cloud technology in businesses, your cloud provider must be capable of ensuring data safety and availability. 5. IDS and IPS: A blend of Intrusion Prevention (IPS) and Intrusion Detection systems (IDS) deters intruders and understands their intentions in advance. 6. Secure Web Practices: Safe web browsing involves several aspects. They are -only using secured sites, watching what you download, not clicking on the pop-ups and ads, and not answering emails that ask you to donate your wealth. 7. Regular backups: They avoid data loss and provide a fallback should when any illicit event happens to your primary device. The cyber security analyst salary demands the cybersecurity professional to perform multiple responsibilities and one of them is supervising regular backups. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript 8. Device Updates and Security Patches: They guarantee that all discovered loopholes are protected. 9. Incident Management: All cybersecurity incidents must be properly recognized and communicated. This offers a log that is effective when determining ways to enhance the existing cybersecurity controls. You can earn an admirable cyber security salary per month if you know how to mitigate most of the above-discussed cyberattacks. Cyber Security Frameworks – Cybersecurity frameworks are a set of policies and procedures businesses can adopt to improve and upgrade its cybersecurity strategies. These frameworks are created by different cybersecurity organizations and government agencies and serve as guidelines for businesses to enhance their cybersecurity. They offer detailed directions on how to implement a five-step cybersecurity process. Identify – Shortlist vulnerable assets within the organization. Protect – Take care of required maintenance to safeguard data and assets. Detect – Detect intrusions and breaches. Respond – Respond to breaches  Recover – Recover from any damage to data, systems, corporate finance, and reputation due to the attack. Check out upGrad’s Advanced Certification in Cloud Computing Learn about: Top 10 Highest Paying IT Certifications in India Cyber Security Skill Gap Besides the increasing demand for cybersecurity professionals, the industry faces an acute shortage of adequately trained individuals capable enough and proficient at filling the mounting cybersecurity roles. According to a NASSCOM report in 2019, India would need around 1 million expert cyber professionals by 2020. Education institutions recognized the gap and started to provide courses in cyber security in India but it is essential to analyze and compare the courses before taking an action.  The forecast by Cybersecurity Ventures indicates a growing recognition among major corporations of the necessity for Chief Information Security Officer (CISO) roles, with a projected rise from 70% in 2018 to 100% in 2021. However, finding qualified people for these roles can be challenging. The job needs diverse skills and knowledge because cyber threats keep changing.  Despite having the most extensive IT talent pool in the world, today, India severely lacks skilled cybersecurity professionals. This acute shortage has resulted in companies’ willingness to pay a premium salary to cybersecurity professionals. According to some reports, the average cybersecurity experts’ salaries in India have grown to INR. 8.8 lakh. There aren’t enough specialized training programs, and many companies compete for the same skilled experts. To fix this, we need better education, more diversity, and clearer career paths for people interested in cybersecurity leadership roles. upGrad’s Exclusive Software and Tech Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Cyber Security Jobs and Salaries in India Following are some of the cybersecurity jobs that are witnessing a sharp increase in the pay. 1. Network Security Engineer It’s a critical position in every organization. This individual is responsible for protecting the organization’s assets from threats and needs to possess astute organizational, technical, and communication skills. The job responsibilities encompass a focus on quality control within the IT infrastructure, including designing, building, and protecting scalable, secure, and robust systems, assisting the company in understanding advanced cyber threats, working on operational data center systems and networks, and helping create robust strategies to protect the organizational structure. They also oversee the maintenance of routers, firewalls, switches, VPNs, and various network monitoring tools. A network cyber security salary in India is INR 4 lakhs to 8 lakhs annually. The high cyber security salary of network security engineers reflects the critical nature of their role in protecting an organization’s network infrastructure, the specialized skill set they possess, and the increasing demand for their expertise in combating cyber threats. Source 2. Cyber Security Analyst A cybersecurity analyst is also referred to as an IT security analyst, security analyst, information system security analyst, or data security analyst. The person is primarily responsible for planning, implementing, and upgrading security controls and measures. The job involves maintaining data, monitoring security access, and protecting information systems and digital files against unauthorized access, modification, and destruction. A security analyst is expected to manage a network, intrusion detection and prevention systems, conduct internal and external security audits, and analyze security breaches to determine their root cause. The individual is also responsible for defining, maintaining, and implementing corporate security policies and coordinate security plans with outside vendors. Further, the person is also responsible for training fellow employees in security procedures by following best practices to avoid security breaches. The salary of a cybersecurity analyst starts from INR. 5 to 6 lakhs per annum. Source 3. Security Architect A security architect plays a primary role in designing the computer and network security architecture for an organization. This person helps with researching, planning, and developing security elements. The individual creates a design based on the company’s needs and then works together with the programming team to make the final structure. Besides planning the architecture, a security analyst is expected to develop organizational procedures and policies regarding how employees should use the security systems and decide on the punitive actions in case of any lapses. Thus, the person is expected to know the business with an in-depth awareness of its technology and information needs. The yearly salary of a security architect starts at INR. 17-22 lakhs. A security architect has specialized skills in creating strong security systems, making them crucial in shaping an organization’s security strategy. Their experience and high demand for their skills contribute to their high pay. Source In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 4. Cyber Security Manager A cybersecurity manager creates strategies to enhance Internet and network security related to various projects. This person is responsible for maintaining security protocols throughout the organization and manages a team of IT professionals to ensure the highest standards of data security are stringently maintained. The individual is expected to frequently review the existing security policies and make sure that the policies are based on new threats. Upskilling through courses in cyber security in India can help you become a manager quickly.   Further, the person performs regular checks on all servers, routers, switches, and other connected devices to confirm that there are no loose ends or lapses in the security system. The person is also expected to hire new employees, prepare and oversee budgets, and evaluate and procure new security technologies and tools. The average annual salary of a cybersecurity manager is INR. 12 lakhs.  The cyber security expert salary in India of managers is because of their leadership in protecting digital assets, strategic decision-making, experience, and the complexity of their role in managing cybersecurity. With the escalating demand for skilled professionals capable of mitigating evolving cyber threats, the pivotal role played by cyber security managers in safeguarding organizations further justifies their higher salaries. Source 5. Chief Information Security Officer (CISO) Also referred to as CSO (Chief Security Executive), CISO is a C-level management executive. A report from PWC states that nowadays, 80+ percent of organizations have a CISO on the management team. A CISO oversees the operations of a company’s IT security department and related staff. This person is responsible for directing and managing operations, strategies, and budgets to safeguard the organization’s information assets. A CISO works with the team to identify, build, implement, and manage organization-wide processes to ensure there are no security breaches. The person is expected to respond to incidents and devise appropriate controls and standards to mitigate security risks maintaining the necessary business continuity. The average annual salary of a CISO is Rs. 23 LPA. CISOs are responsible for aligning cybersecurity with business objectives, managing risks, and making critical decisions, which justifies their higher cyber security salary per month in India. Their expertise, strategic leadership, and the growing demand for seasoned cyber security leaders contribute to the competitive salaries offered for this executive role. Source 6. Ethical Hackers Ethical hackers are responsible for identifying vulnerabilities and security flaws and help businesses from malicious hackers. They work with the authorization of the organizations they work in to ensure that their hacking efforts are legal and legitimate. They are responsible for penetrating computer systems and networks to quickly find and fix computer security vulnerabilities, perform reverse engineering, application analysis, protocol analysis, malware analysis, and debugging. Some ethical hackers begin with IT experience and get Certified Ethical Hacker credentials provided by the International Council of E-Commerce Consultants. The annual salary of an ethical hacker ranges from INR 5 lakhs to 6 lakhs. This demand is further fueled by their capacity to prevent risks, ensure compliance, and their certifications like Certified Ethical Hacker (CEH), which validate their expertise. As businesses increasingly prioritize cybersecurity, ethical hackers with proven skills in identifying system weaknesses and safeguarding against threats are sought after, resulting in higher cybersecurity salaries to attract and retain top talent in this field. Source Factors Affecting Cyber Security Engineer Salary in India Cyber security engineer salaries in India can be affected by multiple factors. Let’s see some primary salary affecting factors: Location A cyber security analyst salary largely varies with the location in which they are working. There are particular cities in India that pay a significantly higher salary to cyber security personnel than the others.  As per the reports of Indeed, cities in which cyber security average salary in India is on the higher side, are: Hyderabad, the average cyber security analyst salary is around 14 lakhs per annum Delhi, the average pay of a cyber security analyst is around 9 lakh per annum Bengaluru, the average salary of a cyber security analyst is around 8 lakh per annum Noida, the average salary of a cyber security analyst is around 8 lakh per annum Chennai, the average pay of a cyber security engineer salary is more than 8 lakh per annum.  Other high-paying cities in India include Mumbai, Gurgaon and Pune 2. Experience Years of experience play a crucial role in determining cyber security engineer salary in India The reports of Payscale show that an entry-level security engineer can earn an average of  INR 3.9 Lakh per annum. On the other hand, any security engineer with more than 10 years of experience can easily earn more than INR 20 lakh per annum.  Those with intermediate experience can also earn a handsome salary of around INR 10 lakh per annum. Apart from continuously upskilling and staying relevant with the norms of the field, one must also need to have relevant years of experience to get promoted to the dream role and build a strong cyber security career in India.  3. Skills In a field like cyber security, which deals with constantly evolving cyber threats, one has to massively focus on their skillsets and continuously upskill them. The fact that there is a huge labour gap in the industry makes it even more important to have the right set of skills. To start, one must focus on the currently popular skills that can get into an entry-level job. Then they can understand the working environment better and upskill themselves as per their own needs as well as the needs of the industry, hence enhancing the cyber security engineer salary in India Payscale shows that presently the most valued skills in the cyber security career include the knowledge of IT security and infrastructure, security information and event management (SIEM), vulnerability assessment, security risk management etc.   Individuals with the aforementioned skillsets can easily earn cyber security jobs salary that is above average in the market.  4. Company Cyber security average salary in India is around 6 lakh per annum, however, it may highly vary with the company they are working in. some cyber security personnel also prefer running their own agencies or starting their cyber security career in India as a freelancer. In each case, the pay is extremely situational.  Cyber security engineers in India can apply for both government and private job roles. Various Indian government organisations such as RAW, Military intelligence, CBI, CERT and various police departments actively recruit cyber security engineers.  Top private companies in India that recruit cyber security engineers in India include Cognizant, Capegemini, Wipro, IBM, Accenture, CSS corporation and so forth.  Essential Skills for Cyber Security Engineer In today’s digital landscape, the role of a cybersecurity engineer has become paramount in safeguarding sensitive information and critical systems from cyber threats. As technology advances, the demand for skilled cybersecurity professionals is rising. So, if you want to become a cyber-security engineer, you must possess the following skills: – Cyber Threat Intelligence Staying one step ahead of cyber adversaries requires a keen understanding of the evolving threat landscape. Cybersecurity engineers must be well-versed in the latest attack vectors, malware trends, and emerging vulnerabilities. This skill helps them anticipate potential threats and design effective defense strategies. Problem-Solving Skills Cybersecurity engineers often face complex and dynamic challenges that require quick and effective problem-solving. They must analyze intricate security incidents, identify root causes, and develop innovative solutions to mitigate risks. Strong analytical thinking and the ability to approach problems from various angles are invaluable in this context. Communication and Collaboration Effective communication skills are crucial for cybersecurity engineers. They must convey technical information clearly to both technical and non-technical stakeholders. Additionally, collaboration is essential as cybersecurity professionals often work in interdisciplinary teams involving IT staff, management, legal teams, and more. Collaborative skills aid in bridging the gap between technical intricacies and business objectives. Ethical Mindset Ethics play a pivotal role in cybersecurity. Engineers must understand their actions’ legal and ethical implications, particularly when performing vulnerability disclosure and penetration testing tasks. A strong ethical foundation ensures their work contributes positively to the digital ecosystem. Continuous Learning The cybersecurity landscape is ever-changing, with new threats and technologies emerging regularly. A successful cybersecurity engineer understands the importance of continuous learning. Staying updated with industry trends, attending workshops, obtaining certifications, and participating in online communities can help professionals remain at the forefront of their field. Top Companies Hiring Cyber-Security Engineers in India Some of the top companies are: – Tata Consultancy Services (TCS): TCS is one of India’s largest IT services companies and offers clients a wide range of cybersecurity services globally. They often hire cybersecurity professionals to help clients secure their digital assets. TCS generally offers a high cyber security fresher salary package because they are required to work on various aspects of cybersecurity, including risk assessment, threat detection, security architecture, and implementing robust defense mechanisms tailored to client’s specific needs. Infosys Another major player in the IT industry, Infosys, hires cybersecurity engineers to work on various aspects of information security, including network security, application security, and data protection. Infosys also offers a  hefty cyber security fresher salary in India because their roles demand them to have expertise in several tasks. Starting with strengthening network security, ensuring application security, and implementing measures for robust data protection, these professionals must work on strategies and solutions to counter emerging cyber threats and vulnerabilities. Wipro Wipro is known for its cybersecurity services and solutions. They hire cybersecurity engineers to develop and implement security measures for their client’s IT infrastructure. The cyber security salary for freshers offered by Wipro is also considerably high because they are tasked with assessing risks, fortifying defenses, and implementing cutting-edge security technologies to combat evolving cyber threats effectively. HCL Technologies HCL provides cybersecurity services to a diverse range of industries. Cybersecurity engineers at HCL work on projects related to security architecture, risk assessment, and threat management. They also receive a high cyber security jobs salary because of their critical role in devising and implementing robust security frameworks aligned with industry best practices. IBM India IBM is a global technology company that offers a range of cybersecurity solutions. They hire cybersecurity professionals to research security, analyze threats, and develop security products. They contribute significantly to enhancing cyber security posture and crafting solutions that address clients’ evolving security needs. Hence, the cyber security salary at IBM is relatively high. Accenture Accenture offers cybersecurity consulting services to clients across various industries. They hire cybersecurity engineers to design and implement security solutions that align with client’s business objectives. Accenture also offers their freshers a hefty cyber security fresher salary because they focus on understanding clients’ unique security challenges and devising strategies that effectively mitigate risks and strengthen defenses. Amazon India As e-commerce and cloud computing continue to grow, Amazon hires cybersecurity engineers to ensure the security of their platforms and services. These professionals work diligently to fortify security measures, safeguard customer data, and ensure the integrity and availability of Amazon’s digital infrastructure. Hence, the cyber security jobs salary at Amazon is considerably high. Microsoft India Microsoft’s presence in India involves cybersecurity-related roles, where engineers work on securing Microsoft’s products and services and providing security solutions to customers. They are usually offered a high cyber security salary in India because they focus on enhancing the security of Microsoft’s offerings, identifying vulnerabilities, and offering robust security solutions aligned with industry standards. Deloitte India Deloitte offers cybersecurity consulting and advisory services to clients. They hire cybersecurity engineers for risk assessments, compliance, and incident response. They play a pivotal role in guiding clients towards fortified cyber security practices. Cisco India Cisco is known for its networking and cybersecurity solutions. They hire cybersecurity professionals for network security, threat intelligence, and cybersecurity research. Cisco offers high cyber security expert salary in India because they contribute to developing cutting-edge security solutions tailored to address clients’ diverse security challenges. How to Pursue a Cyber Security Career? The opportunities for cyber security analysts are currently at their prime in India. To protect against cyber attacks and security breaches, organizations are willing to pay good salary to cyber security experts who can safeguard their company and customers data. It includes the rise of cyber security salary in India. Online courses in cyber security in India are a great option for people who want to upskill but don’t have the option to leave their job. Unlike offline, you can revisit any video anytime. You can learn at your own pace.  With a significantly soaring demand for cybersecurity professionals coupled with the lucrative salaries they offer, a cybersecurity career is becoming one of the most sought-after career options now. If you want to pursue this profession, upGrad and IIIT-B can help you with a Advanced Certificate Programme in Cyber Security . The course offers specialization in application security, cryptography, data secrecy, and network security. This is one of the most popular courses in cybersecurity in India which takes 12 months also consists of live lectures. The course is imparted by world-class faculty members and industry experts and teaches you 6 programming languages and tools. Besides 360-degree career support consisting of the job fair, mock interviews, and more, the course also offers 3 guaranteed job interviews and a dedicated student success mentor. The course consists of over 7 case studies and projects that provide you with the much-needed hands-on experience, besides theoretical knowledge. The course includes a capstone project that validates your understanding and knowledge at the end of the program. It is one of the most successful courses in cybersecurity in India with more than 40,000 learners, 300+ hiring partners and has EMI options for learners.  The course is designed for IT and technology professionals, data professionals, IT and technical project leads and managers, testing professionals, and Java and other coding professionals and prepares you for various cybersecurity domain positions, including cybersecurity engineers, cybersecurity experts, and software developers. You need to have a Bachelor’s degree in attending the course, and no prior coding experience is required. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Refer to your Network! If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to ₹70,000/- You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

by Pavan Vadapalli

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18 Feb 2024

Natural Language Processing (NLP) Projects &amp; Topics For Beginners [2023]
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Natural Language Processing (NLP) Projects & Topics For Beginners [2023]

What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computational techniques to understand, manipulate, and generate human language. These projects harness the power of artificial intelligence and machine learning to process and analyze textual data in ways that mimic human understanding and communication. Here are some key aspects and examples of NLP projects: 1. Text Classification NLP can be used to classify text documents into predefined categories automatically. This is useful in sentiment analysis, spam detection, and topic categorization. For instance, classifying customer reviews as positive or negative to gauge product sentiment. It also plays a crucial role in topic categorization, aiding in the organization and understanding of large volumes of textual data. Natural Language Processing projects help us understand Text Classification better by letting us put theories into action.  Through hands-on projects, we get to apply text classification algorithms to real situations, like figuring out if customer reviews are positive or negative. These projects expose us to different types of text data challenges, such as messy information or imbalanced categories, helping us learn how to handle them. 2. Named Entity Recognition (NER) Named Entity Recognition (NER) is a vital part of Natural Language Processing (NLP) that helps machines identify and categorize specific entities in a given text. NLP models can identify and categorize entities such as names of people, organizations, locations, and dates within text. This is crucial for information extraction tasks like news article analysis or document summarization. Natural Language Processing projects focusing on Named Entity Recognition provide hands-on experience with extracting valuable information from unstructured text. For instance, when analyzing news articles, NER can be applied to pinpoint key entities, making it easier to understand the main players, locations, and dates involved in a story. Projects in NLP also allow practitioners to explore practical applications of NER beyond its standalone use. For instance, integrating NER into larger projects like document summarization or information extraction showcases its versatility and relevance in solving complex NLP challenges. 3. Machine Translation Projects in this domain focus on developing algorithms that translate text from one language to another. Prominent examples include Google Translate and neural machine translation models. The goal of machine translation is to enable seamless communication between people who speak different languages, breaking down language barriers and fostering global understanding. MT systems require extensive training data in multiple languages to learn the patterns and nuances of language pairs. Projects in NLP involve sourcing and preprocessing large bilingual corpora, including translated texts, to train robust translation models. Natural Language Processing projects in machine translation provide a practical understanding of the technical, linguistic, and ethical dimensions involved in building effective translation models, contributing to the ongoing efforts to facilitate cross-language communication in diverse contexts. 4. Text Generation Text generation is a fascinating aspect of Natural Language Processing (NLP) that involves creating coherent and contextually relevant text automatically using computer algorithms. These algorithms can range from traditional rule-based methods to more advanced deep learning models. NLP models like GPT-3 can generate human-like text, making them useful for content generation, chatbots, and creative writing applications. The goal of text generation is to produce human-like text that follows the style and structure of a given language. In NLP based projects, text generation often explores conditional scenarios, where the output is influenced by specific input conditions, making it applicable for tasks like chatbot responses or context-based sentence completion. Data preprocessing plays a pivotal role in preparing diverse and representative datasets for effective model training.  5. Question-Answering Systems Question-Answering (QA) Systems represent a significant area within Natural Language Processing that focuses on developing algorithms capable of comprehending questions posed in natural language and providing relevant and accurate answers. These systems aim to bridge the gap between human language understanding and machine processing, allowing users to interact with computers in a more conversational manner. These nlp project ideas involve building systems that can understand questions posed in natural language and provide relevant answers. IBM’s Watson is a well-known example. NLP project ideas based on QA systems may also explore context-aware systems, where the model considers the broader context of a conversation or passage to provide more accurate answers. 6. Speech Recognition While technically part of the broader field of speech processing, NLP techniques are used in transcribing spoken language into written text, as seen in applications like voice assistants (e.g., Siri and Alexa). These NLP related projects involve the collection of high-quality audio datasets with diverse speakers and linguistic variations that are essential for training robust models. Preprocessing steps involve converting audio signals into a format suitable for analysis, often using techniques like spectrogram representations. NLP projects in Python have diverse applications especially when it comes to speech recognition. They range from the development of voice assistants and dictation software to transcription services and voice-controlled devices. The outcomes contribute significantly to the creation of hands-free interfaces, facilitating accessibility features for differently-abled individuals, and propelling advancements in voice-activated technologies. All in all, there are many easy NLP projects in Speech Recognition that beginners can take up to develop a deeper understanding of spoken language by computers, enhancing human-computer interaction intuitively and expanding accessibility across various applications and user scenarios. 7. Text Summarization NLP can automatically generate concise summaries of lengthy texts, making it easier to digest information from news articles, research papers, or legal documents. NLP based projects in Text Summarization explore different techniques, such as extractive summarization, where the algorithm selects and combines existing sentences, and abstractive summarization, where it generates new sentences to convey the essential meaning. The applications of Text Summarization projects are diverse and impactful. They are used to quickly condense lengthy articles, news, or documents, providing readers with a concise version that captures the main ideas. These projects in NLP essentially empower computers to act as efficient summarizers, making information more accessible and saving time for users who need a quick understanding of complex texts. 8. Sentiment Analysis Analyzing social media data and customer reviews to determine public sentiment toward products, services, or political issues is a common NLP application. NLP project ideas focusing on Sentiment Analysis, algorithms are trained to analyze words and phrases to determine the overall sentiment conveyed by a piece of text. These projects are particularly useful in various applications, such as assessing customer reviews, monitoring social media sentiments, or gauging public opinion. The goal is to help businesses and organizations understand how people feel about their products, services, or specific topics. 9. Language Modeling Language Modeling is a fundamental concept in Natural Language Processing (NLP) that involves teaching computers to understand and predict the structure and patterns of human language Creating and fine-tuning language models, such as BERT and GPT, for various downstream tasks forms the core of many NLP projects. These models learn to represent and understand language in a generalized manner. Language Modeling projects in NLP play a pivotal role in enabling computers to grasp the intricacies of human language, facilitating applications that require language understanding and generation. These projects are essential in various NLP applications, such as speech recognition, machine translation, and text generation. By understanding the structure of language, computers can generate coherent and contextually relevant text, making interactions with machines more natural and human-like. What are the Different Best Platforms to Work on Natural Language Processing Projects? Here are some of the best platforms for nlp projects for final year: 1. Python and Libraries Python is the most popular programming language for NLP due to its extensive libraries and frameworks. Its user-friendly syntax and readability also make it particularly suitable for students with varying programming experience. Therefore, it stands out as an excellent platform to undertake NLP projects for final year students. Libraries like NLTK, spaCy, gensim, and the Transformers library by Hugging Face provide essential NLP functionalities and pre-trained models. In addition, visualization tools like Matplotlib and Seaborn contribute to effective project presentation. Collectively, the combination of Python and its libraries provides a conducive and resource-rich environment for successful Natural Language Processing with Python projects. 2. TensorFlow and PyTorch These deep learning frameworks provide powerful tools for building and training neural network models, including NLP models. Researchers and developers can choose between them based on their preferences. They are powerful tools to aid in building smart computer systems, especially for final year students working on NLP related projects. TensorFlow, made by Google, is known for being flexible and great for big projects on machine learning and deep learning. On the other hand, PyTorch’s dynamic graph is well-suited for research-oriented work. Both frameworks have rich documentation, and active communities, and support a variety of model architectures. 3. Google Colab For cloud-based NLP development, Google Colab offers free access to GPU and TPU resources, making it an excellent choice for training large NLP models without needing high-end hardware. It serves more like a cloud-based platform, offering free access to GPUs and TPUs. It’s akin to a virtual workspace where users can run code, train models, and analyze data without the constraints of computational resources. Its integration with popular libraries like TensorFlow and PyTorch makes it an excellent choice for collaborative and resource-intensive Natural Language Processing projects. 4. SpaCy SpaCy is a fast and efficient NLP library that excels at various NLP tasks, including tokenization, named entity recognition, and part-of-speech tagging. It also offers pre-trained models for multiple languages. SpaCy functions as a language expert in projects involving extensive text data. Its reputation for speed and efficiency makes it a preferred tool for NLP projects for beginners. 5. Docker Docker containers can create reproducible and portable NLP environments, ensuring consistency across development and deployment stages. It acts as a versatile containerization tool, allowing users to package an entire project, along with its dependencies, into a single, reproducible unit. This is particularly advantageous for NLP projects with specific software configurations, ensuring consistency across different environments. Docker addresses the common challenge of project reproducibility. 6. AWS, Azure, and Google Cloud These cloud platforms offer scalable compute resources and specialized NLP services like Amazon Comprehend, Azure Text Analytics, and Google Cloud NLP, simplifying the deployment of NLP solutions at scale. These platforms are like powerful virtual data centers offering computing power services to storage and machine learning tools. AWS is known for its extensive service offerings, Azure seamlessly integrates with Microsoft technologies, and Google Cloud excels in data analytics and machine learning. For students taking up both NLP mini project topics and big project topics, these platforms provide access to cutting-edge technologies without the need for substantial hardware investments. 7. Kaggle Kaggle provides datasets, competitions, and a collaborative platform for NLP practitioners to share code and insights. It’s a great resource for learning and benchmarking NLP models. Like a virtual playground for data scientists, Kaggle provides datasets for analysis, hosts machine learning competitions, and allows users to create and share code through Jupyter notebooks. For students working on NLP projects, it is a collaborative space where they can apply their data science skills in real-world scenarios, learn from others, and build a portfolio that demonstrates their capabilities to potential employers. 8. GitHub GitHub is a repository for NLP project code, facilitating collaboration and version control. Many NLP libraries and models are open-source and hosted on GitHub. Students can host their code repositories on GitHub, track changes, and collaborate with peers. It’s an invaluable tool for final-year projects, facilitating version management, issue tracking, and showcasing their works in natural language processing projects for GitHub to prospective employers. 9. Apache Spark Apache Spark can be used for handling large-scale NLP tasks for distributed data processing and machine learning. Apache Spark is an open-source framework for big data processing, handling tasks like batch processing, streaming, machine learning, and graph processing efficiently. With its in-memory processing and support for multiple languages, it’s a versatile tool for final-year projects dealing with large datasets or complex computations, making tasks scalable and faster. NLP Projects & Topics Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP based projects that match your learning needs. So, we have collated some examples to get you started. So, if you are a ML beginner, the best thing you can do is work on some NLP projects. We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting NLP projects which beginners can work on to put their knowledge to test. In this article, you will find top NLP project ideas for beginners to get hands-on experience on NLP. But first, let’s address the more pertinent question that must be lurking in your mind: why to build NLP projects? When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. NLP is all about analyzing and representing human language computationally. It equips computers to respond using context clues just like a human would. Some everyday applications of NLP around us include spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, etc. As you start working on NLP projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. In the last few years, NLP has garnered considerable attention across industries. And the rise of technologies like text and speech recognition, sentiment analysis, and machine-to-human communications, has inspired several innovations. Research suggests that the global NLP market will hit US$ 28.6 billion in market value in 2026.  When it comes to building real-life applications, knowledge of machine learning basics is crucial. However, it is not essential to have an intensive background in mathematics or theoretical computer science. With a project-based approach, you can develop and train your models even without technical credentials. Learn more about NLP Applications. To help you in this journey, we have compiled a list of NLP project ideas, which are inspired by actual software products sold by companies. You can use these resources to brush up your ML fundamentals, understand their applications, and pick up new skills during the implementation stage. The more you experiment with different NLP projects, the more knowledge you gain. Before we dive into our lineup of NLP projects, let us first note the explanatory structure.  The project implementation plan All the nlp projects for final year included in this article will have a similar architecture, which is given below: Implementing a pre-trained model Deploying the model as an API Connecting the API to your main application This pattern is known as real-time inference and brings in multiple benefits to your NLP design. Firstly, it offloads your main application to a server that is built explicitly for ML models. So, it makes the computation process less cumbersome. Next, it lets you incorporate predictions via an API. And finally, it enables you to deploy the APIs and automate the entire infrastructure by using open-source tools, such as Cortex.  Here is a summary of how you can deploy machine learning models with Cortex: Write a Python script to serve up predictions. Write a configuration file to define your deployment. Run ‘cortex deploys’ from your command line. Now that we have given you the outline let us move on to our list!  Must Read: Free deep learning course! So, here are a few NLP Projects which beginners can work on: NLP Project Ideas This list of NLP projects for students is suited for beginners, intermediates & experts. These NLP projects will get you going with all the practicalities you need to succeed in your career. Further, if you’re looking for NLP based projects for final year, this list should get you going. So, without further ado, let’s jump straight into some NLP projects that will strengthen your base and allow you to climb up the ladder. This list is also great for Natural Language Processing projects in Python.  Here are some NLP project idea that should help you take a step forward in the right direction. 1. A customer support bot One of the best ideas to start experimenting you hands-on projects on nlp for students is working on customer support bot. A conventional chatbot answers basic customer queries and routine requests with canned responses. But these bots cannot recognize more nuanced questions. So, support bots are now equipped with artificial intelligence and machine learning technologies to overcome these limitations. In addition to understanding and comparing user inputs, they can generate answers to questions on their own without pre-written responses.  For example, Reply.ai has built a custom ML-powered bot to provide customer support. According to the company, an average organization can take care of almost 40 % of its inbound support requests with their tool. Now, let us describe the model required to implement a project inspired by this product.  You can use Microsoft’s DialoGPT, which is a pre-trained dialogue response generation model. It extends the systems of PyTorch Transformers (from Hugging Face) and GPT-2 (from OpenAI) to return answers to the text queries entered. You can run an entire DialoGPT deployment with Cortex. There are several repositories available online for you to clone. Once you have deployed the API, connect it to your front-end UI, and enhance your customer service efficiency! Read: How to make chatbot in Python? 2. A language identifier Have you noticed that Google Chrome can detect which language in which a web page is written? It can do so by using a language identifier based on a neural network model.  This is an excellent nlp project in python for beginners. The process of determining the language of a particular body of text involves rummaging through different dialects, slangs, common words between different languages, and the use of multiple languages in one page. But with machine learning, this task becomes a lot simpler. You can construct your own language identifier with the fastText model by Facebook. The model is an extension of the word2vec tool and uses word embeddings to understand a language. Here, word vectors allow you to map a word based on its semantics — for instance, upon subtracting the vector for “male” from the vector for “king” and adding the vector for “female,” you will end up with the vector for “queen.” A distinctive characteristic of fastText is that it can understand obscure words by breaking them down into n-grams. When it is given an unfamiliar word, it analyzes the smaller n-grams, or the familiar roots present within it to find the meaning. Deploying fastTExt as an API is quite straightforward, especially when you can take help from online repositories. 3. An ML-powered autocomplete feature Autocomplete typically functions via the key value lookup, wherein the incomplete terms entered by the user are compared to a dictionary to suggest possible options of words. This feature can be taken up a notch with machine learning by predicting the next words or phrases in your message. Here, the model will be trained on user inputs instead of referencing a static dictionary. A prime example of an ML-based autocomplete is Gmail’s ‘Smart Reply’ option, which generates relevant replies to your emails. Now, let us see how you can build such a feature.  For this advanced nlp projects, you can use the RoBERTa language model. It was introduced at Facebook by improving Google’s BERT technique. Its training methodology and computing power outperform other models in many NLP metrics. To receive your prediction using this model, you would first need to load a pre-trained RoBERTa through PyTorch Hub. Then, use the built-in method of fill_mask(), which would let you pass in a string and guide your direction to where RoBERTa would predict the next word or phrase. After this, you can deploy RoBERTa as an API and write a front-end function to query your model with user input. Mentioning NLP projects can help your resume look much more interesting than others. 4. A predictive text generator This is one of the interesting NLP projects. Have you ever heard of the game AI Dungeon 2? It is a classic example of a text adventure game built using the GPT-2 prediction model. The game is trained on an archive of interactive fiction and demonstrates the wonders of auto-generated text by coming up with open-ended storylines. Although machine learning in the area of game development is still at a nascent stage, it is set to transform experiences in the near future. Learn how python performs in game development. DeepTabNine serves as another example of auto-generated text. It is an ML-powered coding autocomplete for a variety of programming languages. You can install it as an add-on to use within your IDE and benefit from fast and accurate code suggestions. Let us see how you can create your own version of this NLP tool.  You should go for Open AI’s GPT-2 model for this project. It is particularly easy to implement a full pre-trained model and to interact with it thereafter. You can refer to online tutorials to deploy it using the Cortex platform. And this is the perfect idea for your next NLP project! Read: Machine Learning Project Ideas 5. A media monitor One of the best ideas to start experimenting you hands-on NLP projects for students is working on media monitor. In the modern business environment, user opinion is a crucial denominator of your brand’s success. Customers can openly share how they feel about your products on social media and other digital platforms. Therefore, today’s businesses want to track online mentions of their brand. The most significant fillip to these monitoring efforts has come from the use of machine learning.  For example, the analytics platform Keyhole can filter all the posts in your social media stream and provide you with a sentiment timeline that displays the positive, neutral, or negative opinion. Similarly, an ML-backed sift through news sites. Take the case of the financial sector where organizations can apply NLP to gauge the sentiment about their company from digital news sources.  Such media analytics can also improve customer service. For example, providers of financial services can monitor and gain insights from relevant news events (such as oil spills) to assist clients who have holdings in that industry.  You can follow these steps to execute a project on this topic:  Use the SequenceTagger framework from the Flair library. (Flair is an open-source repository built on PyTorch that excels in dealing with Named Entity Recognition problems.) Use Cortex’s Predictor API to implement Flair. We are currently experiencing an exponential increase in data from the internet, personal devices, and social media. And with the rising business need for harnessing value from this largely unstructured data, the use of NLP instruments will dominate the industry in the coming years. Such developments will also jumpstart the momentum for innovations and breakthroughs, which will impact not only the big players but also influence small businesses to introduce workarounds.  Also read: AI Project Ideas and Topics for Beginners Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Natural Language Processing Techniques to Use in Python Making computers read unorganized texts and extract useful information from them is the aim of natural language processing (NLP). Many NLP approaches can be implemented using a few lines of Python code, courtesy of accessible libraries like NLTK, and spaCy. These approaches can also work great as NLP topics for presentation.  Here are some techniques of Natural Language Processing projects in Python –  Named Entity Recognition or NER – A technique called named entity recognition is used to find and categorise named entities in text into groups like people, organisations, places, expressions of times, amounts, percentages, etc. It is used to improve content classification, customer service, recommendation systems, and search engine algorithms, among other things. Analysis of Sentiment – One of the most well-known NLP approaches, sentiment analysis examines text (such as comments, reviews, or documents) to identify whether the information is good, poor, or indifferent. Numerous industries, including banking, healthcare, and customer service, can use it. BoW or Bag of Words – A format that transforms text into stationary variables is called the Bag of Words (BoW) model. This makes it easier for us to convert text to numbers to be used in machine learning. The model is simply interested in the number of terms in the text and isn’t focused on word order. It may be used for document categorisation, information retrieval, and NLP. Cleaning raw text, tokenisation, constructing a vocabulary, and creating vectors are all steps in the normal BoW approach. TF-IDF (Term Frequency – Inverse Document Frequency) – The TF-IDF calculates “weights” that describe how significant a word is in the document.  The quantity of documents that include a term reduces the TF-IDF value, which rises according to the frequency of its use in the document. Simply said, the phrase is rare, more distinctive, or more important the higher the TF-IDF score, and vice versa. It has uses in information retrieval, similar to how browsers try to yield results that are most pertinent to your request.  TF and IDF are calculated in different ways.  TF = (Number of duplicate words in a document) / (Number of words in a document) IDF = Log {(Number of documents) / (Number of documents with the word)} Wordcloud – A common method for locating keywords in a document is word clouds. In a Wordcloud, words that are used more frequently have larger, stronger fonts, while those that are used less frequently have smaller, thinner fonts. With the ‘Wordcloud’ library and the ‘stylecloud’ module, you can create simplistic Wordclouds in Python. This makes NLP projects in Python very successful.  In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses NLP Research Topics –  To ace NLP projects in Python, it is necessary to conduct thorough research. Here are some NLP research topics that will help you in your thesis and also work great as NLP topics for presentation –  Biomedical Text Mining Computer Vision and also NLP Deep Linguistic Processing Controlled Natural Language Language Resources and also Architectures for NLP Sentiment Analysis and also Opinion Mining NLP includes Artificial Intelligence Issues includes Natural language understanding and also Creation Extraction of Actionable Intelligence also from Social Media Efficient Information also Extraction Techniques Use of Rule also based Approach or Statistical Approach Topic Modelling in Web data Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion In this article, we covered some NLP projects that will help you implement ML models with rudimentary knowledge software development. We also discussed the real-world applicability and functionality of these products. So, use these topics as reference points to hone your practical skills and propel your career and business forward!  Only by working with tools and practise can you understand how infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our NLP projects guide to build your very own NLP projects! If you wish to improve your NLP skills, you need to get your hands on these NLP projects. If you’re interested to learn more about machine learning online course, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

by Pavan Vadapalli

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17 Feb 2024

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