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Diving into the world of engineering and data science, I’ve discovered the potential of MATLAB as an indispensable tool. It has accelerated my career and ignited my passion for innovative problem-solving. In this article, MATLAB project ideas for beginners, I aim to share my journey and expertise with those embarking on their path in this dynamic field. My intention is to provide a foundational guide for aspiring professionals eager to delve into technical computing and algorithm development. Through a detailed exploration of MATLAB fundamentals, the acquisition of critical skills via project work, and an understanding of the importance of MATLAB projects for professional development, I hope to simplify the path to mastering MATLAB. This article is designed to make MATLAB approachable and prove its value as a cornerstone in your arsenal of professional tools.  We have MATLAB projects for beginners a gentle approach of multiple skill levels. Whether you’re a beginner or an expert, you’d find a brain-teasing project here. What is MATLAB? “Matrix Laboratory” is the full name of MATLAB. It is a high-performance programming language for technical computing that combines programming, calculation, and visualization in a user-friendly environment. MATLAB is a programming platform for scientists and engineers. It uses the MATLAB language, combining matrix and array mathematics with design processes and iterative analysis. By using MATLAB, you can create algorithms, analyze data, build models, and apply them. MATLAB’s apps, built-in functions, and language allow you to use different methods to solve a particular problem. MATLAB finds applications in many areas, including control systems, communications, machine learning, computational biology, and deep learning.  Top MATLAB Project Ideas with source code The following are some of the most exciting MATLAB projects with source code so that you can test your skills. Let’s get started: 1. Build a Car Parking Indicator Parking a car can be tricky. It requires precision and a lot of practice. You can use MATLAB to make things easier for the driver, however, by building a car parking indicator. You can take inspiration from various parking indicator systems.  An automated car parking indicator would alert the driver when the car is too close to an object. This way, the driver can avoid those objects and turn the vehicle accordingly. You can build a car parking indicator for private parking spaces or open spaces. Such a system can have many benefits: The driver would save time and park his/her car more efficiently. Parking spaces would also be used more efficiently. The chances of a vehicle getting damaged would decrease drastically. Your system can guide the driver to a nearby suitable parking space. You can take it a step further and add the functionality of suggesting a parking space only if it’s available. Maybe your system can determine if a car park has open slots or not, and it can indicate a parking space to the driver of the vehicle accordingly. The sensors can co-ordinate and help in guiding the driver to an open and nearby parking slot. Source Code: Car Parking Indicator 2. Use Artificial Neural Network for Image Encryption Privacy issues have become highly prevalent in recent years. This is one of the best MATLAB project ideas for mechanical engineering for you on this list if you take an interest in cybersecurity and cryptography. You can perform image encryption by taking the help of Artificial Neural Networks (ANNs in short).  Image encryption can prevent unauthorized parties from viewing and accessing images. This way, your data can remain safe. In simple terms, image encryption hides its information. In image encryption, you convert the original plaintext into ciphertext (which can seem like a bunch of nonsense). You can save and transmit this ciphertext over your network, and at the receiver’s end, the ciphertext would convert into the original plaintext.  Neural Networks are machines that behave similarly to how a human brain functions. You can encrypt images on the sender’s end through one ANN and use another ANN to decrypt the image on the receiver’s end. You can use MATLAB to build a complete image encryption system that uses Artificial Neural Networks. After completing this project, you’d be familiar with cryptography as well.  Source Code: Image Encryption 3. Design and Apply an Electronic Differential System An Electronic Differential System allows vehicles to balance them better while turning or running on curved paths. Automotive manufacturers use this system in place of the mechanical differential. This system provides every driving wheel with the required torque and enables multiple wheel speeds.  In a curved path, the vehicle’s inner and outer wheels would have different rotation speeds as the inner wheels would require a smaller radius. An Electronic Differential System uses the motor speed signals and steering wheel command signal to determine the required power for every wheel, so they get the necessary torque. Must Read: Free nlp online course! It’s an advanced technology that offers many advantages, which its mechanical counterpart fails in providing. For example, the electronic differential is lighter than mechanical differential in terms of weight. The wheel with the least traction wouldn’t limit the torque as it would with a mechanic differential. These systems respond faster and offer many functionalities unavailable in the other one, such as traction control. You can use ml projects for final year to design and implement an electronic differential system. You’ll need to create an embedded system design as well for better application. Source Code: Electronic Differential System Also try: 13 Exciting IoT Project Ideas & Topics For Beginners 4. Build a MATLAB Based Inspection System with Image Processing In this project, you’ll build a MATLAB-based inspection system. Machine vision is becoming an accessible technology in the manufacturing industry because of its versatility. And one of the most significant areas where machine vision can find use is in the inspection stage of product development. Quality inspection is necessary to make sure the product doesn’t have any defects.  You can use MATLAB to create an automated inspection system, and you’ll have to employ image processing. With machine vision image processing, you can perform multiple tasks at once: Counting the number of dark and light pixels Discovering blobs of joined pixels in an image Segmenting a part of an image or change the representation Recognizing patterns in an image by matching templates Reading barcode and 2D code. You can perform many other tasks with machine vision. Your automated inspection system would have to determine whether to accept the final product or reject it. It will make the manufacturing process far more efficient and effective.  Source Code: Inspection System with Image Processing Read : 5 Ways Intelligent Automation Helps Your Business Grow 5. Perform Image Encryption and Verification with Chaotic Maps The project is a little different from the one we’ve discussed previously. In this project, you’ll use chaotic maps to encrypt images on the block and steam levels. There is n number of chaotic maps present that generate keys for encryption, so there would be n number of equations involved. Every equation can have n number of constants.  All of these constants would have specific values (random numbers). You can use a neural network to produce a particular series of numbers for image encryption. For image authentication, you’d have to create a simple algorithm to ensure that the sender and receivers are the right people.  Chaotic maps would make the encryption secure through substituting the image with the cover image and encrypting the former n times. Such secure encryption would ensure that your end product remains free from brute force attacks and differential attacks.  Source Code: Image Encryption Using Chaotic Map Also try: Python Project Ideas and Topics 6. Measure an Object’s Diameter in an Image by using MATLAB Computer vision is a prominent field of study. It finds applications in many areas due to its unique utility. You can use MATLAB to measure an object’s diameter in an image.  This application can find uses in many areas where you can’t find the diameter of an object physically. For example, suppose you need to measure the size of a building. In this case, the physical measurement would be nearly impossible, so you’ll need to use computer vision. Your MATLAB script should first import the image, separate the required object from the background, and in the end, use MATLAB functions to find the object’s diameter. While this project might seem quite simple, it will help you showcase your image processing skills while also highlighting your knowledge of multiple MATLAB functions. Source Code: Object’s Diameter in an Image Using MATLAB 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 7. Use MATLAB to Automate Certificate Generation This project is also among the beginner-level MATLAB project ideas for students. In this project, you’ll create an automated certificate generator using MATLAB. Many institutions certify companies based on their performance and achievements. Educational institutions also generate report cards and certificates for their students. You can create an automated certificate generator, which will make this process efficient and straightforward. This project idea might seem too simple, but you can make it complicated by adding the functionality of generating detailed reports for large datasets.  Source Code: Automate Certificate Generation 8. Create Light Animations with MATLAB and Arduino This is one of the beginner level MATLAB projects on our list. In this project, you’ll use MATLAB and Arduino to create a graphical user interface to control the lighting patterns of multiple lights. By controlling their lighting pattern, you can create various light animations. Using a GUI will allow you to perform many other tasks while running the animation.  We recommend using Arduino Uno for this project. It’d be the hardware of this project, and the software would be the Arduino IDE. You can connect the Arduino Uno board with the required lights. After you’ve connected Arduino Uno with MATLAB, you’ll be able to create simple light animations with the same.  It’s an easy project, but it’ll surely help you explore real-life MATLAB applications and help you realize its versatility. After you’ve made simple light animations, you can take this project a step further and add more lights to create more complex animations.  Source Code: Create Light Animations 9. Log Sensor Data in MS Excel This project requires you to use Arduino Uno with MATLAB to log sensor data in MS Excel. You can add LM35 (a temperature sensor) to your Arduino interface, which would connect to MATLAB through ArduinoIO.  Once you’ve connected Arduino with MATLAB, you’ll need to create a program that transmits the sensor’s data into an Excel sheet. You’ll need to have MS Excel installed on your PC to complete this project. Once you’ve finished this project, you’d have a graphic user interface that allows you to see the logs of the sensor data. To take it a step further, you can add more sensors and log their data into the same excel file (or in multiple different files). This project will give you plenty of experience in using GUI with MATLAB.  Source Code: Log Sensor Data in MS Excel 10. Simulate an Artificial Neural Network Artificial Neural Networks are machines that imitate the functioning of a human brain. Their purpose is to mimic the behavior of a mind and act accordingly. In this project, you can simulate an ANN by creating models and training them.  Before you work on this project, you should be familiar with the basic concepts of artificial intelligence and machine learning. You’ll first need to create a data model that takes particular input and generates a particular output. First, you’ll need to train the model by giving it a list of inputs and outputs. Once you’ve prepared the model, you’d give the model a data list with no outputs.  After completing this project, you’d be familiar with artificial intelligence, machine learning, and relevant technologies.  Source Code: Simulate an Artificial Neural Network 11. Analyze and Design an Antenna While everything is becoming wireless, their connectivity relies largely on antennas. An antenna’s design can have a significant impact on its connection, power consumption, and data retention capabilities. The design should make the antenna compact while allowing it to have a substantial beam width to perform information transmission without any loss.  It’s an excellent project for anyone interested in electronics and communications. You should be familiar with the workings of antennas before you work on this project, however. For example, you should know about the ideal antenna pattern and how a real antenna works. You should also be familiar with the Yagi-Uda antenna, which is the most common TV antenna you see on rooftops. You can estimate (approximately) the operating frequency of such an antenna by viewing its length. You can build a MATLAB program that can perform such estimation with high accuracy and give you the required results.  Source Code: Analyze and Design an Antenna 12. Build a Circuit Design Calculator To build a circuit, you must calculate the component values by using the circuit theory and its formulae. Circuit theory is among the oldest and essential branches of electrical engineering. And its calculations take a lot of time and effort. You can create a MATLAB program that can perform those calculations and help an engineer design a better circuit. Not only will such a system save the user a lot of time, but it will also enhance the accuracy of circuit analysis by minimizing human error.  Your program can analyze and figure out circuit design with inductors, transistors, diodes, capacitors, and other critical components. The program can design highly complex circuits and solve problems accordingly.  Source Code: Circuit Design Calculator In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 13. Compress Images without Loss Modern cameras have become capable of taking highly detailed images. But an increase in an image’s level of detail also leads to a rise in its size. That’s why image compression technologies have become prevalent. You can use MATLAB to perform image compression as well.  In this project, you would aim to compress an image without compromising its quality. In other words, you’ll have to perform lossless image compression. To do so, you can use the discrete cosine transform algorithm. To find out how much loss took place while compressing the image, you can derive the mean-square error (also known as MSE) of your process. To implement these algorithms in MATLAB, you’ll have to use the required functions.  Source Code: Compress Images without Loss Also Read: Machine Learning Project Ideas 14. Perform Real-Time Face Detection with MATLAB Face detection can find applications in many areas. You can use face detection capabilities for image enhancement, security, as well as surveillance. While it’s quite natural for us humans to detect faces, we can’t say the same about computers. A simple change in lighting can cause various intra-class variations, that’s why it’s a complicated issue for machines.  You can build a MATLAB-based face detection system, and you can use the Viola-Jones algorithm. There are many other facial recognition algorithms, but we have chosen the viola-jones algorithm for this project.  It first creates a detector object, then takes the primary image, finds the necessary features, and annotates them. This project will give you experience working with facial recognition technology, which has gained popularity in many fields.  Source Code: Real-Time Face Detection with MATLAB Know more: TensorFlow Object Detection Tutorial For Beginners 15. Build Laser Guidance for a Vehicle In this project, you’d develop a program that can use lasers to inform the vehicle of upcoming road conditions. This technology can be really helpful for harsh terrains (such as snowy roads, dirt roads, etc.). You’d need to develop an algorithm in MATLAB that converts the scan sequences into readable data so the user can see what kind of terrain is up ahead. This way, the driver can prepare him or herself accordingly and drive safely. An autonomous vehicle can use this technology, as well.  This project will help you get familiar with the application of MATLAB in automotive engineering. It’ll also help you understand how autonomous vehicles work. You can learn more about this project here.  What are the Skills That You Will Acquire Through MATLAB Projects? Engaging in MATLAB for beginners projects offers a diverse range of skills that are valuable across various industries and fields of study. MATLAB, a powerful programming and numerical computing platform, enables individuals to tackle complex problems, conduct data analysis, and develop innovative solutions. Here are some skills you can acquire through MATLAB project ideas: 1. Programming Proficiency MATLAB simulation projects involve writing code, which helps you develop strong programming skills. You’ll learn about variables, data structures, loops, and conditional statements, which are fundamental concepts in programming. 2. Data Analysis and Visualization It helps in excels in data analysis and visualization. Through projects, you’ll gain expertise in importing, processing, and visualizing data, which is crucial in fields like data science, finance, and engineering. 3. Algorithm Development It allows individual to develop and implement algorithms efficiently. On top of that, you’ll also learn about designing and optimizing algorithms for tasks like, image processing, signal processing, and machine learning. 4. Mathematical Modeling ML is widely used for mathematical modeling and simulations. You’ll acquire skills in creating mathematical models of real-world phenomena and simulating their behavior. 5. Image and Signal Processing MATLAB is renowned for its capabilities in image and signal processing. You’ll learn how to enhance images, analyze signals, and extract meaningful information from them. 6. Machine Learning It offers extensive tools and libraries for machine learning. Through projects, you can develop skills in building and training machine learning models for tasks like classification, regression, and clustering. 7. Numerical Optimization MATLAB is ideal for solving optimization problems. You’ll gain experience in formulating and solving optimization problems, which are valuable in engineering and operations research. 8. Simulink Simulink, a MATLAB toolbox, is used for modeling and simulating dynamic systems. You can acquire skills in system modeling and control design, which are essential in fields like robotics and control engineering. 9. Parallel and Distributed Computing MATLAB allows you to leverage parallel and distributed computing resources. Learning to distribute your computations efficiently is valuable for handling large datasets and complex simulations. 10. Problem-Solving Skills The projects often involve tackling real-world problems. You’ll develop problem-solving skills by breaking down complex challenges into manageable tasks and finding creative solutions. 11. Collaboration and Documentation Working on projects in MATLAB encourages collaboration and the documentation of your code and findings, which are essential skills for teamwork and knowledge sharing. 12. Project Management Managing and completing MATLAB projects requires organizational skills, time management, and goal setting, which are transferable to various professional settings. Why Opt for MATLAB Projects? Engaging in MATLAB project ideas offers several compelling reasons: 1. Practical Application MATLAB is a versatile platform used in academia and industry for solving real-world issues. Through projects, you can apply theoretical knowledge to practical scenarios, enhancing your understanding and skills. 2. Skill Development MATLAB projects cultivate a wide range of skills, including programming, data analysis, and mathematical modeling, which are highly transferable and sought after in many professions. 3. Interdisciplinary Applications MATLAB is not limited to a specific field; it’s used in diverse domains such as engineering, finance, biology, and physics. This versatility allows you to explore various areas of interest and adapt your skills to different contexts. 4. Research Opportunities MATLAB is a common tool in research. Engaging in MATLAB projects can open doors to research collaborations, enabling you to contribute to cutting-edge advancements in your field of study. 5. Career Advancement Proficiency in MATLAB can be a valuable asset on your resume, making you more attractive to employers in technical and scientific fields. 6. Problem-Solving MATLAB projects often involve complex problem-solving, honing your ability to analyze challenges, devise solutions, and make informed decisions. 7. Portfolio Building Completing MATLAB projects creates a portfolio showcasing your practical skills and problem-solving abilities, which can impress potential employers or academic institutions. 8. Personal Growth Working on projects in MATLAB fosters perseverance, creativity, and self-confidence as you overcome obstacles and see tangible results. Join the ML Courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. 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 Learn More About MATLAB Exploring MATLAB Project Ideas for beginners equips you with a practical understanding of MATLAB and significantly enhances your analytical and computational skills. Delving into projects ranging from simple calculations to complex data analysis and visualization offers an invaluable hands-on experience in today’s data-driven world. Whether you‘re a student stepping into programming and engineering or a professional seeking to refine your skills, MATLAB projects offer a versatile platform for learning and innovation. This guide aims to inspire and equip beginners with a diverse range of project ideas, showcasing the potential of MATLAB in solving real-world problems. As you embark on this journey, remember that each project is a step towards mastering a tool that is indispensable in engineering and science.  If you’re interested to learn more about MATLAB, machine learning, and its relevant topics, 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. You’ll find plenty of valuable resources to answer your questions. Refer to your Network! 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The reliability and quality of your research depend upon several factors such as determination of target audience, the survey of a sample population, choice of technique and methods for research, and analysis of results. The answer to all these questions is the research design. An effective research design creates minimum discrepancies in data and improves trust in the research information that is collected and analyzed. An impactful research design offers a very less margin for errors and ensures accuracy in all types of design. This blog decodes the types of research design, its elements, and salient features. Check out our artificial intelligence free courses to get an edge over the competition. Research design, simply put, is a specification related to the processes and methods for gaining information needed. It is an operational pattern that is overall a framework of any project. This states precisely what information could be gathered or collected from a source by the use of specific methods. The research design also helps to gain insights into the methods to be employed for the same. This is why you must be wondering if and which research design is diagnostic in nature? What is Research Design? To begin every important research, the researcher chooses a structure of techniques and methods to be applied to the research process. This structure or framework is called research design. Research design enables researchers to hone the ideal research methods for the topic at hand and establish an environment for successful research studies.  Benefits Of Research Design Any research design functions like a bridge that connects what has already been established, that is, research objectives, to what actually has to be performed as part of the conducted study. The final goal is to achieve the specific objectives. It tries to gauge what a client will need regarding findings and arrive at the analytical work on gathered data that will convert these into relevant findings. Are There Different Types Of Research Design? If there is no explicit design, a researcher will have ideas that are fogged around what needs to be performed. Thus, it is extremely advisable that any design gets put up on paper since it is obvious that any study might go down the drain if the concepts exist in a researcher’s mind only. Unless the research design is clear and written or drawn up well, the research actually becomes flaky in terms of structure. This is why there must be clarity to help understand the research design types. The research design topic explains the type of research, whether it is experimental, correlational, survey, semi-experimental, or review. It also defines the sub-type, be it research problem, experimental design, descriptive case study, etc. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. The three most common research design types are data Collection, measurement, and analysis. The research design type is determined by the research problem faced by the organization, and the design phase of the research study determines what and how to use research tools.  What Are The Features Of Research Design? Any research design is a strategy that suggests the specific means by which you can provide answers for any of the research questions, test any hypotheses, and also achieve research purposes that help select from the apt decision alternatives. This helps solve any management problem and even take advantage of an existing market opportunity, besides helping understand which research design is diagnostic in nature and the research design types. Among the types of research design, a formal design has advantages that are specifically appreciated when an investigator decides on the required data. If the data collected among types of research design, leads to being irrelevant, the result is inefficient as well as puzzling. It is more serious when you end up ignoring some data that might have been necessary. This could be revealed only very later in the stage of analysis. While understanding what are the features of research design, remember that all research design, including research design is diagnostic in nature, actually provides a number of advantages while studying data and also understanding their meaning and research design types. This helps in keeping calculations and also thinking on a path to recommendations and solutions. Remember, this in no way means that design must be in a rigid framework. A good design will guide and never dictate or conduct any research. Main Elements of Research design Research design is a blueprint for data collection, measurement, and analysis. It is the stepping stone for decisions regarding what, where, how much, when, and steps for research design are undertaken.  Featured Program for you: Fullstack Development Bootcamp Course A research design is a schedule of conditions for data collection and evaluation designed to align with the research objectives and available resources.  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. Research design is a plan that recognizes the kinds and sources of information related to the research problem. Research design is a strategy that indicates the methods to be adopted for collecting and analyzing data.  The research design also includes the time and cost budgets constraints that are primary to any research study. FYI: Free nlp course! Although all types of research design have some common characteristics, the key elements that every good research design have are: Purpose statement Data collection techniques Methods of research data analysis Type of research methodologies Possible obstacles to the research Settings for research study Time of the research study Analysis measurement Salient features of Research Design Accurate and reliable results drive the validity of research design. But unfortunately, many companies tend to make crucial decisions driven by skewed research. The main reason for this is the failure of researchers to consider biased research. To achieve 100% accuracy in your research data, you must do everything to safeguard your research against any bias.  Moreover, your research outcome must apply to a wide population rather than a small sample. To guarantee a wide reach, you must get accurate models and account for any margin of error.  The essential characteristics of research design are: 1. Neutrality  When you set up the research study, you may need to make some assumptions about the data collected, and the results projected must remain neutral and free of any bias. 2. Reliability  In regularly conducted research, the researcher will expect similar results each time. Your research design must ensure that the research questions help maintain the standard of results. You can attain the desired results only if your research design is reliable.  3. Validity  The researcher must leverage the right measuring tools to gauge the accuracy of the research results and check whether the results align with the research objectives.  Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. 4. Generalization  The outcome of your research design must apply to an entire population and not just a confined sample. A generalized design ensures a full-proof design that can be applied to any population with the same level of accuracy. Five Common Types of Research Design  Research design can be categorized into several main types based on their method and purpose: exploratory, descriptive, causal, diagnostic, and experimental studies. 1. Descriptive Design  In this hypothesis-based design methodology, the researcher primarily describes the subject matter central to the research. Descriptive research design applies to natural observations, case studies, and surveys. This method involves data collection, data analysis, and its presentation. It allows the researcher to put forth the problem to persuade others to comprehend the necessity for the research.  2. Correlational Design  True to its name, correlational research design enables the researchers to establish relationships between two related variables. This type of research design method needs at least two data groups. This method can be utilized for observational studies.   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 3. Experimental Design  Be it a quasi-experiment, a field, or a controlled experiment, this research design type establishes a clear cause and the effect of any event. The researcher studies the impact of the independent variable on the dependent variable.  Typically, this research design type is applied to solve a specific issue by altering the independent variables and observing the changes to the dependent ones. For instance, an experiment can be performed on the price change, and its effect on customer gratification can be observed. 4. Diagnostic Design  In diagnostic research design, the researchers strive to explore the underlying reason for the occurrence of certain circumstances. This method can assist you in examining in depth the elements that cause specific challenges that your customers may face. The design generally comprises three phases, namely the problem inception phase, problem diagnosis phase, and the problem solution phase.     5. Explanatory Design  As the name suggests, explanatory design is utilized by researchers to explore, expand and explain theories and innovative ideas. This design method is applied to find the missing pieces of a puzzle or obtain clarity on vague aspects of a certain topic.  Qualitative and Quantitative Research Design Here’s a table summarizing the differences between qualitative and quantitative research design based on the provided descriptions: Aspect Qualitative Design Quantitative Design Purpose Understand how and why events occur. Answer questions like who, what, where, how many, and when. Questions Open-ended questions. Closed-ended questions. Data Nature Non-numeric, descriptive data. Numeric, can be quantified. Data Collection Gathered through detailed surveys, interviews, or observations. Collected via structured surveys, tests, or experiments. Analysis Explains concepts, ideas, and behaviors. Analyzed through statistics, graphs, and charts. Use in Research Ideal for exploring ideas and understanding behaviors. Best for measuring and comparing numerical data. Business Application Understanding customer mindsets and behavior. Gathering actionable data for decision-making. Output Rich, in-depth insights and narratives. Quantifiable data and statistical information. Examples Ethnography, case studies, focus groups. Surveys with rating scales, experiments, and polls. Flexible and Fixed Research Design Yet another research design classification is the flexible and fixed research design. Fixed research design is based on quantitative data collection, and flexible research design is based on qualitative data collection. In a static design method, the design is settled and fixed before data collection. With a flexible research design, you have more freedom for collecting data.  For instance, survey questions with multiple choice answers are a part of fixed research design, while surveys, where respondents need to type their answers, are a part of flexible research design.   what are the 4 types of research design based on grouping? Another allocation of research design is made based on the grouping of participants. Mostly, grouping is based on the hypothesis of the research and the sampling of participants. Given below are the four main research design types based on grouping: 1. Cohort Design Study A cohort research design study is a longitudinal research study that takes samples of a cohort group of people (those with similar characteristics). In this kind of panel study, the panel individuals share certain common characteristics. 2. Cross-sectional Design Study In a cross-sectional research study, data is analyzed either from an entire population or from a typical sample at one instance.  3. Longitudinal Design Study A longitudinal research design study comprises of similar observations of the same variants for a short period or a longer period. A longitudinal method is often touted as a kind of observational research study though they may also be characterized as random experiments.   In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 4. Cross-Sequential Design Study Cross-sequential design study combines both cross-sectional and longitudinal analysis, working to make up for some inherent issues present in both research design methodologies.    An exploratory research design example could involve conducting in-depth interviews with industry experts to uncover emerging market trends and understand customer preferences, laying the groundwork for future detailed studies. How upGrad can help you in this? upGrad offers Masters’ and Doctorate courses in Business Administration with topics in research design studies and methodologies.  The programs offer best-in-class content instructed by leading faculty and industry leaders. upGrad has contributed to successful career transitions for over 100+ learners partnering with over 300+ hiring partners. 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 summing up our exploration of the Types of Research Design, it becomes clear that understanding the various frameworks is crucial for conducting thorough and impactful research. From qualitative to quantitative methodologies and from flexible to fixed designs, each type offers unique advantages tailored to specific research objectives. Elements such as the research question, data collection methods, and analysis techniques are foundational to selecting an appropriate design that aligns with the study’s goals. Additionally, grouping-based designs provide a structured approach to experimental and non-experimental research. As we have seen, the choice of research design profoundly influences the validity, reliability, and overall success of a study. Embracing the diversity of these designs is essential for researchers aiming to contribute valuable insights into their fields. 

Humans have made several attempts to mimic the biological systems, and one of them is artificial neural networks inspired by the biological neural networks in living organisms. However, they are very much different in several ways. For example, the birds had inspired humans to create airplanes, and the four-legged animals inspired us to develop cars. The artificial counterparts are definitely more powerful and make our life better. The perceptrons, who are the predecessors of artificial neurons, were created to mimic certain parts of a biological neuron such as dendrite, axon, and cell body using mathematical models, electronics, and whatever limited information we have of biological neural networks. Checkout: Artificial Intelligence Project Ideas Components and Working of Biological Neural Networks Image caption: Parts of a biological neural network Image source A Biological Neural Network diagram illustrates the interconnected neurons in the brain, highlighting synapses, dendrites, and axons. This model aids in understanding neural processing and learning in biological systems. In living organisms, the brain is the control unit of the neural network, and it has different subunits that take care of vision, senses, movement, and hearing. The brain is connected with a dense network of nerves to the rest of the body’s sensors and actors. There are approximately 10¹¹ neurons in the brain, and these are the building blocks of the complete central nervous system of the living body. The neuron is the fundamental building block of neural networks. In the biological systems, a neuron is a cell just like any other cell of the body, which has a DNA code and is generated in the same way as the other cells. Though it might have different DNA, the function is similar in all the organisms. A neuron comprises three major parts: the cell body (also called Soma), the dendrites, and the axon. The dendrites are like fibers branched in different directions and are connected to many cells in that cluster. Dendrites receive the signals from surrounding neurons, and the axon transmits the signal to the other neurons. At the ending terminal of the axon, the contact with the dendrite is made through a synapse. Axon is a long fiber that transports the output signal as electric impulses along its length. Each neuron has one axon. Axons pass impulses from one neuron to another like a domino effect. Biological neural networks in machine learning aim to replicate the brain’s structure and function using artificial neurons and learning algorithms like backpropagation. They model complex cognitive processes but lack biological fidelity whereas Biological neural networks in soft computing focus on mimicking brain-inspired computing for tasks requiring human-like decision-making or pattern recognition, integrating fuzzy logic, genetic algorithms, and neural networks to enhance adaptability and intelligence in systems. Learn AI Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Why Understand Biological Neural Networks? For creating mathematical models for artificial neural networks, theoretical analysis of biological neural networks is essential as they have a very close relationship. And this understanding of the brain’s neural networks has opened horizons for the development of artificial neural network systems and adaptive systems designed to learn and adapt to the situations and inputs. Image caption: An artificial neuron Image source 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 Biological Neural Networks vs Artificial Neural Networks The human brain consists of about 86 billion neurons and more than 100 trillion synapses. In artificial neural networks, the number of neurons is about 10 to 1000. But we cannot compare biological and artificial neural networks’ capabilities based on just the number of neurons. There are other factors also that need to be considered. There are many layers in artificial neural networks, and they are interconnected to solve classification problems. Biological neural networks tolerate a great deal of ambiguity in data. However, artificial neural networks require somewhat precise, structured, and formatted data to tolerate ambiguity. Biological neural networks are fault-tolerant to a certain level, and the minor failures will not always result in memory loss. FYI: Free nlp course! The brain can recover and heal to an extent. But the artificial neural networks are not designed for fault tolerance or self-regeneration. We can still sometimes recover by saving the model’s current weight values and continuing the training from the saved state.  Talking about power consumption, the brain requires about 20% of all the human body’s energy, equivalent to about 20 watts, which is exceptionally efficient. But computers need an enormous amount of computational power to solve the same problem, and they also generate a lot of heat during computation. Artificial neural networks were inspired by the biological neural networks of the human body. The modeling of biological neural networks was a crucial step in the development of artificial neural networks. Many scientists attempted to understand the working of the brain. Artificial neural networks today are being used for various applications, some are biologically related, and most of them are engineering related. Even though biological neural networks and artificial neural networks are similar in function, they still have many differences. Many attempts have been made to understand the complex mechanism of biological neural networks. Yet, they still hold many secrets to unlock and inspire the future of artificial intelligence. Differences Between Biological Neural Networks (BNNs) and Artificial Neural Networks (ANNs) Parameter Biological Neural Networks (BNNs) Artificial Neural Networks (ANNs) Basic Unit Neuron: The fundamental cell responsible for processing and transmitting information in the brain. Artificial Neuron (Node): Simplified mathematical models that simulate neuron functions. Signal Transmission Electrochemical signals via synapses: Neurons communicate using electrical impulses and chemical signals across synapses. Numerical values via weighted connections: Nodes transmit information through numerical weights. Learning Mechanism Hebbian learning, synaptic plasticity: Learning involves changes in the strength of synapses based on activity patterns. Backpropagation, gradient descent, etc.: Learning adjusts weights using algorithms to minimize error. Processing Speed Relatively slow (milliseconds to seconds per signal): Biological neurons have slower transmission speeds due to the nature of chemical and electrical processes. Fast (microseconds to milliseconds per computation): Computational nodes process information rapidly, limited by hardware. Energy Efficiency Very efficient, low power consumption: The brain operates on about 20 watts, highly efficient for its complexity. Less efficient, high computational power required: ANNs, especially large models, consume significant power. Scalability Naturally scalable and self-organizing: BNNs can grow and reorganize their connections as needed. Requires significant resources for large-scale models: Scaling ANNs involves more hardware and computational power. Plasticity High, can rewire and adapt over time: The brain’s structure and function can change significantly through neuroplasticity. Low, fixed structure during inference after training: Once trained, the ANN’s structure remains mostly static. Information Encoding Spike timing and frequency (temporal coding): Information is encoded in the timing and frequency of neuron spikes. Continuous numerical values (usually in range [-1, 1]): Information is represented as numerical values in a continuous range. Robustness Highly robust, capable of coping with damage: The brain can often function effectively despite damage or loss of neurons. Susceptible to performance degradation with changes: ANNs can be sensitive to changes in input data or architecture. Parallel Processing Naturally parallel, many neurons firing simultaneously: BNNs process information in a massively parallel manner. Simulated parallelism using CPUs or GPUs: ANNs achieve parallelism through hardware that simulates concurrent operations. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Conclusion If you are curious to master Machine learning and AI, boost your career with an our Master of Science in Machine Learning & AI with IIIT-B & Liverpool John Moores University. 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? 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The AI market has witnessed rapid growth on the international level, and it is predicted to show a CAGR of 37.3% from 2023 to 2030. The production system in artificial intelligence is the key driver of this surge due to AI integration across industries to enhance process optimization and decision-making. These figures point to the growing ubiquity of AI and highlight the importance of understanding what is behind its success, especially in production systems.  When I talk about production systems in AI, I’m essentially referring to structured frameworks that play a pivotal role in modeling logical rules and knowledge. These systems are vital for achieving specific AI application goals by processing input data and generating understandable outputs. Essentially, they mimic human thinking, comprising a set of rules, a knowledge base, and an inference engine. It’s fascinating how they’re shaping the future of AI and driving innovation across industries.  Production System in AI A production system (popularly known as a production rule system) is a kind of cognitive architecture that is used to implement search algorithms and replicate human problem-solving skills. This problem-solving knowledge is encoded in the system in the form of little quanta popularly known as productions. It consists of two components: rule and action. Rules recognize the condition, and the actions part has the knowledge of how to deal with the condition. In simpler words, the production system in AI contains a set of rules which are defined by the left side and right side of the system. The left side contains a set of things to watch for (condition), and the right side contains the things to do (action). Types of Production Systems There are three common types of basic production systems: the batch system, the continuous system, and the project system. What are the Elements of a Production System? An AI production system has three main elements which are as follows:   Global Database: The primary database which contains all the information necessary to successfully complete a task. It is further broken down into two parts: temporary and permanent. The temporary part contains information relevant to the current situation only whereas the permanent part contains information about the fixed actions.   A set of Production Rules: A set of rules that operates on the global database. Each rule consists of a precondition and postcondition that the global database either meets or not. For example, if a condition is met by the global database, then the production rule is applied successfully.   Control System: A control system that acts as the decision-maker, decides which production rule should be applied. The Control system stops computation or processing when a termination condition is met on the database. So the components of the production system includes Global database, A Set of production rules and Control system. What are the Features of a Production System? A production system has the following features: Simplicity: Due to the use of the IF-THEN structure, each sentence is unique in the production system. This uniqueness makes the knowledge representation simple to enhance the readability of the production rules. Modularity: The knowledge available is coded in discrete pieces by the production system, which makes it easy to add, modify, or delete the information without any side effects. Modifiability: This feature allows for the modification of the production rules. The rules are first defined in the skeletal form and then modified to suit an application.     Knowledge-intensive: As the name suggests, the system only stores knowledge. All the rules are written in the English language. This type of representation solves the semantics problem. 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Here are key strategies:  Depth-First Search (DFS):  DFS is a sequential search and gives suboptimal solutions due to deep paths.  Breadth-First Search (BFS):  BFS is a level-by-level systematic exploration that ensures completeness but requires additional memory.  Best-First Search:  Best-First Search chooses the best paths that have good heuristic values for making informed decisions.  Rule Ordering and Priority:  Rule ordering and priority is a configurable strategy that determines the sequence or priority of rule execution.  Parallelism and Concurrency:  Improves performance as it executes multiple rules in parallel.  Production system characteristics in Artificial Intelligence is rule-based frameworks where a set of conditions and actions (or productions) are used to derive conclusions or perform tasks, facilitating automated decision-making and problem-solving. Production System Rules  We have seen the production system and the types of production systems in artificial intelligence. Now let’s explore some rules:  Condition-Action Structure:  Production rules have a condition-action or “if-then” structure. When a specific condition occurs, an action is performed. This logical framework enables the system to react intelligently depending on different inputs.  Rule Base:  The total set of production rules in a system forms the rule base. This repository includes the knowledge and logic implemented in the AI system to make decisions.  Inference Engine:  The inference engine, an important part of a production system, determines the conditions described within rules and instantiates corresponding actions. It performs rule-based reasoning and decision-making.  Conflict Resolution:  In cases where several rules could apply, a conflict resolution mechanism emerges. This helps to ensure that the system focuses on rules and actions, thus eliminating ambiguity.   Forward and Backward Chaining:  Production systems use forward chaining, in which rules are applied using available data, or backward chaining, where the system works backward from a goal to determine which rules to apply.  What are the Classes of a Production System? A production system is classified into four main classes which are:   Monotonic Production System: In a monotonic production system, the use of one rule never prevents the involvement of another rule when both the rules are selected at the same time. Hence, it enables the system to apply rules simultaneously.   Partially Commutative Production System: In this production system if a set of rules is used to change state A to state B then any allowable combination of these rules will also produce the same results (convert state A to state B).   Non-Monotonic Production System: This production system increases the problem-solving efficiency of the machine by not keeping a record of the changes made in the previous search process. These types of production systems are useful from an implementation point of view as they do not backtrack to the previous state when it is found that an incorrect path was followed.   Commutative Production System: These type of production systems is used when the order of operation is not important, and the changes are reversible. 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. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Examples of Production Systems in Artificial Intelligence  Below are some examples of types of production systems in AI: Expert Systems:  Classic production systems in AI includes expert systems. These systems simulate the reasoning capabilities of human experts in particular domains. They take a rule-based approach, in which the inference engine is governed by a knowledge base of rules that draw conclusions and deliver expert advice. They include medical diagnosis systems and financial advisory systems.  Manufacturing Control Systems:  In manufacturing, AI-based production systems are used to control and maximize the performance of the process. Rules determine parameters for machinery adjustment, inventory management, and quality control. These systems improve efficiency and responsiveness in dynamic manufacturing environments.  Customer Support Chatbots:  Customer support chatbots use production rules in order to interact with the users according to predetermined conditions. The responses to the user’s queries are based on rules, which dictate how the chatbot should act by giving information or referring users to a supervisor. These systems boost customer relations and streamline support processes.  What are the Advantages of using a Production System in AI?   Offers modularity as all the rules can be added, deleted, or modified individually.   Separate control system and knowledge base.   An excellent and feasible model that imitates human problem-solving skills.   Beneficial in real-time applications and environment.   Offers language independence. 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 The exploration of the production system in artificial intelligence (AI) reveals its significance as a fundamental framework for problem-solving and knowledge representation. By dissecting its elements, features, control/search strategies, rules, and classes, we have gained a comprehensive understanding of how production systems operate within AI. The myriad examples presented illustrate the versatility and adaptability of production systems in various AI applications, showcasing their ability to efficiently process information and execute tasks based on a set of rules. Furthermore, the advantages of using a production system in AI, including enhanced decision-making capabilities, scalability, and the facilitation of complex problem-solving, underscore its value in the development of intelligent systems. As we continue to push the boundaries of technology, the role of the production system in artificial intelligence will undoubtedly evolve, offering new opportunities for innovation and advancement in the field. This exploration not only demystifies the concept but also highlights its potential to revolutionize how we interact with and leverage technology in the AI-driven era. Check out Advanced Certification Program in Machine Learning & Cloud with IIT Madras, the best engineering school in the country to create a program that teaches you not only machine learning but also the effective deployment of it using the cloud infrastructure. Our aim with this program is to open the doors of the most selective institute in the country and give learners access to amazing faculty & resources in order to master a skill that is in high & growing

In this article, you will learn about AI vs Human Intelligence, Difference Between AI & Human Intelligence. Definition of AI & Human Intelligence Comparison of AI & Human Intelligence Nature Functioning Learning power What AI cannot do without – The “human” factor Artificial Intelligence vs. Human Intelligence: What will the future hold? Read more to know each in detail. Artificial Intelligence has come a long way from being a component of science fiction to reality. Today, we have a host of intelligent machines like self-driving cars, smart virtual assistants, chatbots, and surgical robots, to name a few. Since AI became a mainstream technology in the present industry and a part of the common man’s daily life, it has sparked a debate – Artificial Intelligence vs. Human Intelligence.  While Artificial Intelligence seeks to design and create intelligent machines that can perform human-like tasks, one cannot help but think, “Is Artificial Intelligence sufficient in itself?” Perhaps the biggest fear is that AI will “replace” humans and outsmart them in a few years. However, it is not entirely true. Although AI is highly advanced – now that machines can learn from experience and make smart decisions – AI cannot function optimally without relying on innately human attributes like human intuition.  Now, let’s dig deeper into the Artificial Intelligence vs Human Intelligence group discussion to understand their peculiarities and relationship. Artificial Intelligence vs Human Intelligence: Table of Comparison  In my journey through the evolving landscape of technology, I’ve encountered numerous instances that highlight the differences and intersections between AI and human intelligence. The comparison below draws on real-life scenarios and case studies to shed light on this ever-relevant debate of AI vs human intelligence.  Parameter Artificial Intelligence (AI) Human Intelligence (HI) Origin Created by humans using algorithms and programming Innate, evolved through biological processes Learning Learns from data and experience through algorithms Learns through experience, reasoning, and intuition Speed Capable of processing vast amounts of data quickly Processing speed varies; slower than AI for some tasks Flexibility Adaptable to specific tasks with programmed rules Highly adaptable and can generalize across tasks Creativity Can simulate creativity based on learned patterns Can demonstrate originality and novel solutions Emotion Lacks emotional intelligence and empathy Emotionally aware and capable of empathy Errors May make errors due to incorrect data or algorithms Prone to errors but can learn from mistakes Biases Reflects biases present in training data and algorithms Biases can exist but can be consciously mitigated Energy Efficiency Requires energy but can optimize processing efficiency Efficient in terms of energy use compared to AI Context Understanding Limited context awareness without explicit programming Understands context naturally in various situations Self-awareness Lacks self-awareness and consciousness Self-aware and conscious of existence Artificial Intelligence vs Human Intelligence: Definition What is Artificial Intelligence? Artificial Intelligence is a branch of Data Science that focuses on building smart machines capable of performing a wide range of tasks that usually require human intelligence and cognition. These intelligent machines are imbued with learning from experience and historical data, analyzing their surrounding environments, and performing befitting actions. Vendors have now been scrambling to promote their commodities via AI. This helps them to accelerate their business intensively. There are several subsets like machine learning, R, Java, and other popular programming languages. AI is an interdisciplinary science that leverages concepts and tools from multiple fields like computer science, cognitive science, linguistics, psychology, neuroscience, and mathematics.  Read: Future Scope of Artificial Intelligence What is Human Intelligence? Human Intelligence refers to humans’ intellectual capability that allows us to think, learn from different experiences, understand complex concepts, apply logic and reason, solve mathematical problems, recognize patterns, make inferences and decisions, retain information, and communicate with fellow human beings.  What makes human intelligence unique is that it is backed by abstract emotions like self-awareness, passion, and motivation that enable humans to accomplish complex cognitive tasks. Human intelligence is not just constricted to a particular pattern but can be changed depending upon the problems that arise with it. It can change substantially with the crux of the situation. Artificial Intelligence vs Human Intelligence: A comparison Here’s a head-to-head comparison between Artificial Intelligence and Human Intelligence: Nature While Human Intelligence aims to adapt to new environments by utilizing a combination of different cognitive processes, Artificial Intelligence aims to build machines that can mimic human behavior and perform human-like actions. The human brain is analogous, but machines are digital. In Artificial Intelligence Human Intelligence, Artificial Intelligence aims to provide a style of work efficiency that will help to solve problems without any hassle. It can solve any kind of problem in the blink of an eye whereas, Human Intelligence takes a lot of time to accustom to the mechanisms with a considerable amount of time. So, to see to it, the main difference between natural and artificial intelligence is the process of functionality and the time taken by both of them. Functioning  Humans use the brain’s computing power, memory, and ability to think, whereas AI-powered machines rely on data and specific instructions fed into the system.  Besides, it takes a very long time for humans to process and understand the problems and gets accustomed to them. In the case of Artificial Intelligence proper inputs and study, it helps them to provide accurate result in the end. 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 Learning power Human Intelligence is all about learning from various incidents and past experiences. It is about learning from mistakes made via a trial and error approach throughout one’s life. Intelligent thought and intelligent behavior lie at the core of Human Intelligence. However, Artificial Intelligence falls behind in this respect – machines cannot think. Hence, in this case of the difference between AI vs Human brain, Human Intelligence has a much more powerful thinking capacity than Artificial Intelligence and can have great problem-solving skills depending on the crux of the situation. They can learn from data and through continuous training, but they can never achieve the thought process unique to humans. While AI-powered systems can perform specific tasks quite well, it can take years for them to learn a completely different set of functions for a new application area.  Artificial Intelligence, can learn from data and through continuous training, but it can never achieve the thought process unique to humans. While AI-powered systems can perform specific tasks quite well, it can take years for them to learn a completely different set of functions for a new application area. Join the ai and ml courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. What AI cannot do without – The “human” factor Artificial Intelligence vs. Human Intelligence debate isn’t a fair one. Granted that AI has helped develop intelligent machines that can outperform humans in some respects (case in point – AlphaGo and DeepBlue), they have yet to go a very long way to match the human brain’s potential. Although AI systems are designed and trained to mimic and simulate human behavior, they cannot make rational decisions like humans. Besides, talking about the difference between human and Machine Intelligence, Human Intelligence is the main contributing factor that has given definition to the simulations that are created in Machine Intelligence. So, the main difference between natural and Artificial Intelligence is the data that has been fed to them with the limited problem-solving skills which are offered in this regard. Also Read: AI Project Ideas & Topics The decision-making power of AI systems is primarily based on events, the data they’re trained on, and how they are related to a particular event. AI machines cannot understand the concept of “cause and effect” simply because they lack common sense. Nick Burns, an SQL Services Data Scientist, puts it quite well: “No matter how good your models are, they are only as good as your data…” Humans possess the unique ability to learn and apply their acquired knowledge in combination with logic, reasoning, and understanding. Real-world scenarios require a holistic, logical, rational, and emotional approach that is specific to humans. Therefore, in some aspects of the difference between human and Machine Intelligence, human intelligence seems to be much more feasible than others. Must Read: Free nlp online course! In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses AI vs Human Intelligence: What will the future hold? Right now, AI is still developing and advancing. The time required to train AI systems is considerably high, which isn’t possible without human intervention. Be it autonomous cars and robots, or sophisticated technologies like natural language processing and image processing, they all rely on human intelligence.  Human Intelligence is the one to create AI in the first place. Therefore, it totally depends upon the modifications made by human intelligence to introduce new features in AI. Therefore, to look into the age-long debate about which is better, human intelligence holds much more credibility for creating methods of Machine Learning. Presently, automation is the leading AI application that’s penetrating the industry rapidly. In a 2018 report by the WEF, the Swiss Think Tank predicted that by 2022, AI would displace 75 million jobs globally while also creating 133 million new jobs. The new job profiles will demand Data Science specific skills like knowledge of Mathematics & Statistics and ML algorithms, proficiency in programming, data mining, data wrangling, software engineering, and data visualization. In 2022, WEF has made several agendas of how Artificial Intelligence is the future and it will enormously help us in creating an environment with accurate data. The field has provided feasible job opportunities to people, which has helped them to turn their careers around. The field of Artificial Intelligence is now massive and has been supported immensely with the new branches to learn different components of Data Science. In comparison to Artificial Intelligence Human Intelligence is much more customisable and might not be feasible enough to solve a complicated problem as a whole.   Today, companies using Big Data and Data Science technologies are skilled experts like ML Engineers, Data Scientists, Data Engineers, etc., who know the nitty-gritty of AI/ML. It is the domain knowledge and versatile skillset of such experts that create value out of Big 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 Impact of AI on the Future of Jobs As we delve into the impact of AI on the future of jobs in 2024, I find myself deeply engrossed in understanding how this technological evolution is reshaping our professional landscapes. Drawing from my own experiences in the field, I’ve observed firsthand how AI is altering the job market:  Automation’s Double-Edged Sword: AI’s role in automating tasks has spiked efficiency but also necessitated a shift in workforce skills. In manufacturing, for example, the focus has shifted from manual labor to supervisory roles, spotlighting the nuanced interaction between AI and human intelligence.  New Careers Emerge: Far from merely displacing jobs, AI is pioneering new fields, like AI ethics and machine learning engineering, reflecting a new era of human-AI collaboration.  Skill Shifts: The growing need for AI literacy across sectors, such as healthcare, demands professionals versed in both technology and its ethical application, bridging the gap between AI capabilities and human needs.  Synergistic Workforces: Beyond the AI vs human intelligence debate, there’s a trend towards collaborative innovation, with AI enhancing human creativity and decision-making in fields like creative industries.  Reflecting on these points, it’s clear that AI is not just shaping our future jobs but also redefining the essence of human intelligence and creativity in the workplace.  How is AI different from human intelligence? AI, created by humans through algorithms and programming, learns from vast amounts of data and predefined rules, enabling it to excel in tasks like data analysis, automation, and pattern recognition. However, AI lacks emotional intelligence and consciousness, operating purely based on programmed instructions. In contrast, human intelligence is biological and innate, integrating emotions, creativity, intuition, and consciousness. Humans possess the ability to adapt knowledge across various domains, exhibit complex social interactions, and navigate unpredictable situations with nuanced understanding. AI vs human intelligence examples refers to instances showcasing AI’s efficiency in tasks like data processing and pattern recognition, contrasting with human abilities in creativity, emotional intelligence, and contextual understanding, which AI struggles to replicate. Will AI Replace Humans?  In 2024, the AI vs human intelligence debate intensifies, yet from my standpoint, fearing AI will replace humans overlooks the essence of our synergy. My experiences underscore that AI excels in data analysis and routine tasks, transforming rather than eliminating jobs. Consider healthcare: AI aids in diagnosing diseases with unparalleled precision, but human doctors make the final calls, blending AI’s insights with their empathy and ethical considerations.   Similarly, in finance, AI algorithms might detect investment trends, but human experts, understanding market subtleties and human emotions, take the strategic decisions. This juxtaposition highlights not a rivalry but a partnership between AI and human intelligence. AI handles the heavy lifting of data, while humans contribute creativity, emotional intelligence, and moral reasoning.  As we progress through 2024, it becomes evident that the future hinges not on AI usurping human roles but on how effectively we can integrate AI to amplify human capabilities. This collaborative approach promises not just enhanced efficiency but also the potential for groundbreaking innovations in every field.  Wrapping up The exploration of AI vs Human Intelligence unveils a landscape marked by profound differences and complementary strengths. AI is an invaluable tool shaping the industry, and automation, coupled with intelligent workflow, will be the norm across all sectors in the near future. And while AI has mastered intelligent behavior quite well, it cannot mimic a human’s thought process. So, in this case, AI has been lagging behind when competing as AI vs human brain, which can only solve problems according to the interfaces that are provided in this regard. The human brain is still the mastermind for creating different aspects of simulations and inputs that will be looked after in the AI and help in progressing the concept of Machine Learning. Since scientists and researchers still don’t know the mystery behind the human thought process, it is highly unlikely that we’ll create machines that can “think” like humans anytime soon. To conclude, the future of AI will be governed mainly by human abilities. It will be complemented by human intelligence and cognizance.  Checkout upGrad’s Advanced Certificate Programme in Machine Learning & NLP. This course has been crafted keeping in mind various kinds of students interested in Machine Learning, offering 1-1 mentorship and much more. This will provide you feasible inputs depending upon the subject, and it, therefore, helps you to get in-depth knowledge about the different branches of Data Science paired with aspects of Machine Learning.

Artificial Intelligence or AI career opportunities have escalated recently due to its surging demands in industries. The hype that AI will create tons of jobs is justifiable. A career in AI looks more promising than any other job available these days. Artificial Intelligence is, therefore, a lucrative job opportunity that will help in the advancement of the career opportunities of the aspirants massively. But, before one gets to know about the career opportunities that are enclosed in the field of Artificial Intelligence it is important to know what Artificial Intelligence is and what AI careers are that you can pursue to find the best jobs in AI and take the lead in life.  Employers need AI talent to fulfil the company’s technological requirements. Thus, a career in AI is not only attracting the job-seekers towards it but is witnessing immense growth. To establish a career in AI, job-hunters need to possess relevant technical skills. This will therefore help them to identify the accurate candidate possessing the skillset to work in the field of job roles in Artificial Intelligence. Abundant AI career opportunities are present owing to wide applications in different fields. It is also a matter of confusion among many AI enthusiasts. Thus, we have compiled a list of promising AI career opportunities. Therefore, this will help you gain a keen insight into what courses to choose from to land jobs in AI.  Learn Machine Learning online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Career Opportunities in Artificial Intelligence 1. Big Data Engineer The role of a Big Data Engineer is to create an ecosystem for the business systems to interact efficiently. Their primary task is to build and effectively administer big data of an organization. They also have to carry out the function of obtaining outcomes from big data in a robust manner. Being a big data engineer will fetch you a good salary when compared to other AI roles. An average salary of a Big Data Engineer is ₹895,560. Hence, it could be understood that these jobs in Artificial Intelligence are bound to provide you with massive salary opportunities. In Spark and Hadoop systems, a big data engineer deals with preparing, managing, and establishing a big data environment. The role is suitable for those who are keen to play with new technical tools and can step above the relational database box. Be it for freshers or for people who already have a working knowledge of Big Data, this is a perfect field of work for an individual. This will also elevate the scope and the learning opportunities of different branches of Machine Learning Skills, which are one of the major subsets of careers in AI.  Programming languages like Python, R and Java are essential in building your career in AI as a big data engineer. Skills related to SQL and Apache Spark enhances your chances of grabbing the relevant career opportunity. Aspirants should have some proper insights on data migration along with data visualization and mining. This will thus help in creating concrete knowledge for the people about Artificial intelligence and modifications to create these programs hand-in-hand. Applicants who have a PhD in the field of Computer Science or Mathematics are given more preferences. It is quite evident that one can flourish in their career in AI by being a Big Data Engineer. You can also check out upGrad’s AI courses to inch closer to your AI career goal. Average Data Engineer Salary: Source Average Data Engineer Salary based on Locations: City Salary Bangalore ₹ 11.2 Lakhs New Delhi ₹ 11.1 Lakhs Mumbai ₹ 10.0 Lakhs Hyderabad ₹ 10.6 Lakhs Pune ₹ 9.9 Lakhs Noida ₹ 10.3 Lakhs Gurgaon ₹ 12.5 Lakhs Chennai ₹ 10.4 Lakhs Source Average Data Engineer Salary based on Experience: Experience Salary 1 Year ₹ 6.5 Lakhs 2 Year ₹ 7.6 Lakhs 3 Year ₹ 9.1 Lakhs 4 Year ₹ 11.3 Lakhs 5 Year ₹ 13.8 Lakhs Source Average Data Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 9.3 Lakhs Software Product ₹ 11.5 Lakhs. Internet ₹ 14.1 Lakhs Financial Services ₹ 11.1 Lakhs Analytics & KPO ₹ 11.7 Lakhs Source 2. Business Intelligence Developer The primary responsibility of a Business Intelligence Developer is to consider the business acumen along with AI. They recognize different business trends by assessing complicated data sets. This is done with the help of gaining simulated data that has been fed beforehand to the AI and obtaining concrete results out of it. This helps in providing them with brand recognition and brand awareness in a much more robust technique. They help in swelling the profits of a company by preparing, developing and nourishing business intelligence solutions. Business profitability and efficiency are the two significant factors of development considered by them. They also assist in optimizing different processes and workflow across the organization. Their demands have intensified recently due to their capabilities in dealing with complicated data of cloud-based platforms. This provides a keen insight into the original standpoint of the business. This will leave room for improvement and help them understand what are the remedies required to be made for an appropriate business model.  One who is aware of computer programming and data sets can acquire this position. A formal bachelor’s degree in the field of computers, mathematics or engineering is suitable to land you in a job. The problem-solving strengths and analytical capabilities of the applicants should be high. Sound knowledge of SQL servers and queries along with data warehouse designing is needed while pursuing this career option. The role also pays quite well and their demands won’t diminish in the near future. This makes it one of the major AI careers opportunity. The average salary of a Business Intelligence Developer is ₹ 7.2 Lakhs. This is a tad bit less than a Big Data Engineer, but there are tons of room for improvement and development to have successful careers in AI. Average Business Intelligence Developer Salary: Source Average Business Intelligence Developer Salary based on Locations: City Salary Bangalore ₹ 7.4 Lakhs Pune ₹ 6.0 Lakhs Mumbai ₹ 6.1 Lakhs Hyderabad ₹ 6.5 Lakhs New Delhi ₹ 5.9 Lakhs Noida ₹ 5.7 Lakhs Gurgaon ₹ 6.1 Lakhs Chennai ₹ 6.5 Lakhs Source Average Business Intelligence Developer Salary based on Experience: Experience Salary 1 Year ₹ 5.1 Lakhs 2 Year ₹ 5.6 Lakhs 3 Year ₹ 6.7 Lakhs 4 Year ₹ 8.0 Lakhs 5 Year ₹ 9.8 Lakhs Source Average Business Intelligence Developer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 6.4 Lakhs Software Product ₹ 8.4 Lakhs Internet ₹ 12.3 Lakhs Financial Services ₹ 6.1 Lakhs Analytics & KPO ₹ 6.9 Lakhs Source 3. Data Scientist Data scientists assist in gathering relevant data from multiple sources for the purpose of assessing it to gain constructive inferences. The inferences gained are influential in tackling various issues concerned with the business. Depending upon different data patterns, past and present information, data scientists make various predictions. It is a robust system that helps analysts analyze the root cause and the drawbacks that are caused in the business and how to mitigate and restrain them to happen from in the future, making it one of the most reliable Artificial Intelligence careers.   The performance of the business is positively impacted due to a data scientist. Job-seekers are required to be equipped with modern tools like Spark, Hadoop, Pig or Hive while pursuing this career option. The candidate must be comfortable using programming languages like Python, Scala or SQL. In terms of education, the applicant should hold a master’s degree in Mathematics or Computer Science. Any advanced degree is likely to increase the chance of getting the job. Significant experience in the field of machine learning is mandatory. To interact with the managers, data scientists should possess sound communication skills. Their analytical abilities should be outstanding. Many large technical firms need data scientists to carry out important tasks for the growth of the company. A career in AI thus opens the door to be a Data Scientist. The average annual salary of a Data Scientist is ₹ 14.4 Lakhs. The career is very lucrative and with the increment of several certificate courses on AI has increased the job roles in Artificial Intelligence immensely. If you possess the above skills, then you are a perfect cut-out for the job.  Source Average Data Scientist Salary: Source Average Data Scientist Salary based on Locations: City Salary Bangalore ₹15.5 Lakhs New Delhi ₹13.6 Lakhs Mumbai ₹13.2 Lakhs Hyderabad ₹14.8 Lakhs Pune ₹12.8 Lakhs Chennai ₹13.3 Lakhs Noida ₹13.7 Lakhs Gurgaon ₹14.1 Lakhs Source Average Data Scientist Salary based on Experience: Experience Salary 1 Year ₹9.5 Lakhs 2 Year ₹10.5 Lakhs 3 Year ₹11.6 Lakhs 5 Year ₹16.4 Lakhs 8 Year ₹19.9 Lakhs Source Average Data Scientist Salary based on Industry: Industry Salary IT Services ₹13.2 Lakhs Internet ₹18.3 Lakhs Software Product ₹16.6 Lakhs Financial Services ₹15.1 Lakhs KPO ₹15.3 Lakhs Source 4. Machine Learning Engineer Machine Learning (ML) is known to be a subset of Artificial Intelligence. It runs simulations with the different data that has been given and generate accurate results. Machine learning engineers are involved in building and maintaining self-running software that facilitates machine learning initiatives. They are in continuous demand by the companies and their position rarely remains vacant. They work with huge chunks of data and possess extraordinary data management traits. They work in the areas of image and speech recognition, prevention of frauds, customer insights, and management of risks. To become a machine learning engineer, one must have sound command in applying predictive models dealing with magnificent data.  Programming, computing, and mathematics are essential to becoming successful as a machine learning engineer. The average salary of a Machine Learning Engineer is about ₹10.2 Lakhs. A master’s degree in Mathematics or Computer Science is preferred. Python, R, Scala, and Java are the required technology stacks. Having in-depth knowledge about machine learning algorithms, neural networks and deep learning is highly beneficial. Sound familiarity with software development tools, cloud applications, and excelling coding skills gives you an added advantage. Moreover, you can also make use of upGrad’s top-notch AI and ML courses to sharpen your skills. If you possess the above skills then these jobs in Artificial Intelligence are bound to bring you good fortune. Average Machine Learning Engineer Salary: Source Average Machine Learning Engineer Salary based on Locations: City Salary Bangalore ₹10.5 Lakhs New Delhi ₹9.2 Lakhs Mumbai ₹8.6 Lakh Hyderabad ₹10.1 Lakh Pune ₹8.4 Lakh Chennai ₹8.8 Lakh Noida ₹9.0 Lakhs Gurgaon ₹10.6 Lakh  Source Average Machine Learning Engineer Salary based on Experience: Experience Salary 1 Year ₹7.0 Lakh 2 Year ₹7.8 Lakh 3 Year ₹9.4 Lakhs 4 Year ₹12.2 Lakhs 5 Year ₹15.1 Lakhs Source Average Machine Learning Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹9.5 Lakhs Internet ₹13.8 Lakhs Software Product ₹12.2 Lakhs Financial Services ₹9.1 Lakhs Analytics & KPO ₹14.6 Lakhs Source Must Read: Free deep learning course! 5. Research Scientist Research scientists undertake efforts in performing extensive research dealing with applications of machine learning and machine intelligence. A research scientist is one who has gained expertise in the field of applied mathematics, statistics, deep learning, and machine learning. The average salary of a Research Scientist is ₹9.1 Lakhs.  Applicants are expected to have a PhD degree or advanced master’s degree in mathematics or computer science. The salary of a research scientist is quite high and organizations recruit those who have a good experience in their AI career. Significant knowledge of Natural Language Processing (NLP) and Reinforcement Learning is essential while applying for the role. Aspirants who possess insights regarding parallel computing, computer perception, benchmarking, graphical models, and distributed computing are favoured. It is quite evident that the importance of research scientists will not diminish in the next generation. The courses offered by upGrad are inclined in such a manner that learners can develop skills with ease. AI courses are surely going to give a tremendous boost to your career in AI. Average Research Scientist Salary: Source Average Research Scientist Salary based on Locations: City Salary Bangalore ₹ 10.7 Lakhs Pune ₹ 9.7 Lakhs Mumbai ₹ 9.1 Lakhs Hyderabad ₹ 8.3 Lakhs New Delhi ₹ 7.7 Lakhs Chennai ₹ 7.9 Lakhs Ahmedabad ₹ 8.9 Lakhs Gurgaon ₹ 7.0 Lakhs Source Average Research Scientist Salary based on Experience: Experience Salary 2 Years ₹ 6.2 Lakhs 3 Years ₹ 7.7 Lakhs 5 Years ₹ 8.6 Lakhs 6 Years ₹ 9.4 Lakhs Source Average Research Scientist Salary based on Industry: Industry Salary Pharma ₹ 8.5 Lakhs Manufacturing ₹ 8.8 Lakhs Biotech & Life Sciences ₹ 10.1 Lakhs Clinical Research ₹ 8.1 Lakhs Biotechnology ₹ 8.3 Lakhs Source 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 6. AI Data Analyst The major function of an AI data analyst is to perform data mining, data cleaning, and data interpretation. By cleaning data, requisite data is collected to carry out data interpretation. Any sort of useless data is discarded by them so that it does not hamper the data interpretation process.  With the help of statistical tools and methods, inferences are drawn from the data by an AI data analyst. To become an AI data analyst, you are required to possess a bachelor’s degree in mathematics or computer science. A comprehensive understanding of regression and proficiency in MS Excel is essential to acquire positions in Artificial Intelligence jobs salary. Compared to other AI roles, salaries for AI data analysts are relatively lower, with an average of ₹6.4 lakh. Despite stable demand, the future outlook for AI data analysts is uncertain. These roles are crucial across industries, emphasizing their importance in Artificial Intelligence job markets. Source Average Data Analyst Salary: Source Average Data Analyst Salary based on Locations: City Salary Bangalore ₹6.9 Lakh Mumbai ₹6.7 Lakh Hyderabad ₹6.6 Lakh Chennai ₹6.6 Lakh New Delhi ₹6.7 Lakh Pune ₹6.4 Lakh Gurgaon ₹7.1 Lakh Noida ₹6.7 Lakh Kolkata ₹6.6 Lakh Source Average Data Analyst Salary based on Experience: Experience Salary 1 Year ₹4.6 Lakh 2 Year ₹5.3 Lakh 3 Year ₹6.0 Lakh 4 Year ₹6.8 Lakh 5 Year ₹7.7 Lakh Source Average Data Analyst Salary based on Industry: Industry Salary Internet ₹7.5 Lakh Analytics & KPO ₹7.1 Lakh IT Services & Consulting ₹6.1 Lakh Software Product ₹6.8 Lakh Financial Services ₹7.1 Lakh Source 7. Product Manager In the field of AI, the duty of a product manager is to resolve challenging problems by strategically collecting data. You should possess the skill of identifying relevant problems that obstructs the business proceedings. The next step is to get hold of related data sets to facilitate data interpretation. There are various jobs after artificial intelligence and apart from AI one can get into and product manager is one of those. After data interpretation, the product manager is required to estimate business impacts from the outcomes of data interpretation. Each organization needs a product manager, whose demands have skyrocketed these days. This is one of the most high-paying jobs in Artificial Intelligence. The salary is about on average ₹21.3 Lakh. Average Product Manager Salary: Source Average Product Manager Salary based on Locations: City Salary Bangalore ₹23.0 Lakh Mumbai ₹19.8 Lakh Gurgaon ₹23.6 Lakh New Delhi ₹20.4 Lakh Hyderabad ₹22.4 Lakh Pune ₹20.4 Lakh Noida ₹23.0 Lakh Chennai ₹22.4 Lakh Kolkata ₹18.6 Lakh Source Average Product Manager Salary based on Experience: Experience Salary 2 Year ₹15.1 Lakh 3 Year ₹17.2 Lakh 4 Year ₹18.4 Lakh 5 Year ₹19.3 Lakh 7 Year ₹20.3 Lakh 10 Year ₹23.0 Lakh 12 Year ₹24.2 Lakh Source Average Product Manager Salary based on Industry: Industry Salary Internet ₹24.5 Lakh Financial Services ₹17.8 Lakh IT Services & Consulting ₹24.4 Lakh Software Product ₹26.2 Lakh Banking ₹16.5 Lakh Manufacturing ₹19.5 Lakh Retail ₹27.1 Lakh Pharma ₹12.4 Lakh Insurance ₹14.8 Lakh Telecom ₹23.6 Lakh Source You can also check out our free courses offered by upGrad in Management, Data Science, Machine Learning, Digital Marketing, and Technology. All of these courses have top-notch learning resources, weekly live lectures, industry assignments, and a certificate of course completion – all free of cost! In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 8. AI Engineer AI engineers are problem-solvers who develop, test and apply different models of Artificial Intelligence. They effectively handle AI infrastructure. They make use of machine learning algorithms and understanding of the neural network to develop useful AI models. The artificial intelligence career as an engineer is also highly in-demand. One can get business insights with these models and this helps the company to make effective business decisions. Undergraduate or postgraduate degrees in the field of data science, computer science or statistics are mandatory. Any kind of certifications on ML or data science adds to the advantage. Proficiency in programming languages, such as Python, R or C++, is essential. The applicants should have a strong grasp in statistics, NLP, applied mathematics, and analytics. The pay scale of an AI engineer is quite good due to the role’s surging demands. The average salary is about ₹12.3 Lakh, which ranges less based on other opportunities in Artificial Intelligence careers. Average AI Engineer Salary: Source Average AI Engineer Salary based on Locations: City Salary Bangalore ₹ 13.5 Lakhs Pune ₹ 18.0 Lakhs Mumbai ₹ 19.0 Lakhs Hyderabad ₹ 13.5 Lakhs New Delhi ₹ 10.4 Lakhs Chennai ₹ 7.4 Lakhs Source Average AI Engineer Salary based on Experience: Experience Salary 1 Year ₹ 6.8 Lakhs 2 Year ₹ 9.1 Lakhs 3 Year ₹ 11.8 Lakhs 4 Year ₹ 13.1 Lakhs 5 Year ₹ 16.3 Lakhs Source Average AI Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 12.8 Lakhs Software Product ₹ 9.3 Lakhs Internet ₹ 14.4 Lakhs Financial Services ₹ 19.9 Lakhs Analytics & KPO ₹ 14.7 Lakhs Source Read: AI Engineer Salary in India 9. Robotics Scientist A reduction in jobs will indeed take place due to the emergence of robotics in the field of AI. Conversely, jobs will also rise as robotics scientists are in incessant demands by major industries for programming their machines. The robots will help in carrying out certain tasks efficiently. The candidate should have a master’s degree in robotics, computer science, or engineering.  There are various job opportunities for Artificial Intelligence in India which one can procure and set themselves up for a successful career. The average salary of a Robotics Engineer is about ₹ 4.0 LPA. The median salary for a robotics scientist is quite high. Although automation is favoured by robots, there should be some professionals to build them. Thus, the risk of losing jobs is minimized. Average Robotics Engineer Salary: Source Average Robotics Engineer Salary based on Locations: City Salary Bangalore ₹ 4.6 Lakhs Pune ₹ 4.0 Lakhs Mumbai ₹ 4.2 Lakhs Hyderabad ₹ 6.0 Lakhs Gurgaon ₹ 4.2 Lakhs Chennai ₹ 3.8 Lakhs New Delhi ₹ 3.9 Lakhs Source Average Robotics Engineer Salary based on Experience: Experience Salary 1 Year ₹ 3.9 Lakhs 2 Year ₹ 4.0 Lakhs 3 Year ₹ 4.2 Lakhs 4 Year ₹ 4.4 Lakhs 5 Year ₹ 5.1 Lakhs Source Average Robotics Engineer Salary based on Industry: Industry Salary Manufacturing ₹ 4.2 Lakhs Auto Components ₹ 3.5 Lakhs Industrial Machinery ₹ 3.5 Lakhs Automobile ₹ 3.8 Lakhs Industrial Automation ₹ 5.6 Lakhs Source 10. NLP Engineer The NLP Engineers specialise in the human language which includes both written and spoken languages. It is used by engineers who work on speech recognition, voice assistants, document processing, and so on. The top skills required for NLP Engineers are Python, Java, R, ML methods, ML frameworks and libraries and so on. The average salary for the NLP Engineer is 8.9 lakhs per annum. There are various artificial intelligence job opportunities available in the NLP. Average NLP Engineer Salary: Source Average NLP Engineer Salary based on Locations: City Salary Bangalore ₹ 9.2 Lakhs Pune ₹ 7.6 Lakhs New Delhi ₹ 6.8 Lakhs Hyderabad ₹ 10.6 Lakhs Noida ₹ 9.7 Lakhs Source Average NLP Engineer Salary based on Experience: Experience Salary 2 Year ₹ 7.2 Lakhs 3 Year ₹ 8.9 Lakhs 4 Year ₹ 11.9 Lakhs 6 Year ₹ 17.4 Lakhs Source Average NLP Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 8.0 Lakhs Software Product ₹ 7.9 Lakhs Internet ₹ 7.9 Lakhs Financial Services ₹ 11.4 Lakhs Analytics & KPO ₹ 6.8 Lakhs Source 11. UX Developer Organisations are turning digital, which demands their digital presence to be totally updated and user-friendly. They understand the big picture and are behind the organisational structure. They utilise their skills to create an interactive website. They create the user interface for the app and gather users before designing ideas that can be communicated using storyboards.  A career in artificial intelligence is successful also when the users indulge themselves in developing their skills. The skills required for UX Designers are Research Information Architecture Wireframing  Visual Communication Prototyping  The average salary procured by the UX Developer is ₹ 6 lakhs per annum. Average UX Developer Salary: Source Average UX Developer Salary based on Locations: City Salary Bangalore ₹ 8.0 Lakhs Pune ₹ 10.8 Lakhs. Mumbai ₹ 7.2 Lakhs Hyderabad ₹ 5.8 Lakhs Gurgaon ₹ 5.9 Lakhs Chennai ₹ 7.0 Lakhs New Delhi ₹ 2.8 Lakhs Source Average UX Developer Salary based on Experience: Experience Salary 1 Year ₹ 3.0 Lakhs 2 Years ₹ 5.5 Lakhs 3 Years ₹ 6.3 Lakhs 4 Years ₹ 5.9 Lakhs 5 Years ₹ 7.1 Lakhs Source Average UX Developer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 6.0 Lakhs Software Product ₹ 7.2 Lakhs Internet ₹ 8.0 Lakhs Manufacturing ₹ 5.1 Lakhs Financial Services ₹ 7.8 Lakhs Source 12. Researcher The researchers can work with various AI scientists and different teams and develop new methodologies and ways to advance technology. The career opportunities in artificial intelligence as a researcher are very advanced and high paying. The Researchers get a salary of ₹ 7.7 lakhs per annum. Average Researcher Salary: Source Average Researcher Salary based on Locations: City Salary Bangalore ₹ 9.1 Lakhs Pune ₹ 10.6 Lakhs Mumbai ₹ 9.9 Lakhs Hyderabad ₹ 7.3 Lakhs Kolkata ₹ 9.6 Lakhs Chennai ₹ 3.7 Lakhs New Delhi ₹ 9.4 Lakhs Source Average Researcher Salary based on Experience: Experience Salary 1 Year ₹ 5.6 Lakhs 2 Years ₹ 6.4 Lakhs 3 Years ₹ 8.5 Lakhs 4 Years ₹ 9.1 Lakhs 5 Years ₹ 10.1 Lakhs Source Average Researcher Salary based on Industry: Industry Salary IT Services & Consulting ₹ 8.8 Lakhs Education & Training ₹ 5.7 Lakhs Internet ₹ 9.1 Lakhs Financial Services ₹ 11.1 Lakhs Analytics & KPO ₹ 4.9 Lakhs Source 13. Data Mining The data miners perform strategic data analysis and research. They also indulge in identifying the opportunities that improve productivity via statistical analysis and modelling. There are various AI jobs in Data Mining, one can upskill themselves and move up the ladder. The top skills which are required for Data Miners are Python, R SQL Infrastructure Management  Quantitative Modelling Marketing Analytics Big Data  Artificial Intelligence  Average Data Mining Salary: Source Average Data Mining Salary based on Locations: City Salary Bangalore ₹ 1.8 Lakhs Mumbai ₹ 2.0 Lakhs Hyderabad ₹ 2.8 Lakhs New Delhi ₹ 2.7 Lakhs Source Average Data Mining Salary based on Experience: Experience Salary 1 Year ₹ 2.2 Lakhs 2 Years ₹ 2.4 Lakhs 4 Years ₹ 3.0 Lakhs Source Average Data Mining Salary based on Industry: Industry Salary IT Services & Consulting ₹ 2.6 Lakhs Software Product ₹ 4.0 Lakhs BPO ₹ 2.2 Lakhs Consulting ₹ 1.5 Lakhs Source Companies that make use of AI The top 10 companies make use of AI and are recruiting for the same –  Google (Alphabet Inc.) Amazon Microsoft Apple Facebook (Meta Platforms) IBM Tesla Salesforce Netflix AlibabaIt is an apt time for professionals who want to jump into the field of AI and make their career out of it. Renowned startups like Argo AI are also hiring for significant Artificial Intelligence roles. Source Vast Applications of AI   Healthcare: Proper diagnosis and treatment are facilitated by introducing AI in healthcare. Education: A suitable learning environment is furnished to the students by utilizing AI. Sports: With advanced AI technologies, athletes can expand their capabilities. Agriculture: Maximum yield is possible by AI as it helps in developing the perfect farming environment. Construction: Buildings can be constructed more safely and efficiently by the incorporation of AI. Banking: Chat-bot assistance, fraud detection, and enhanced payment methods are some of the positive outcomes of AI. Marketing: The sales target can be effectively achieved by making use of predictive intelligence along with machine learning. E-commerce: Effective warehouse operations, good product recommendations, and fraud prevention are some of the fruits of AI. Future of AI According to the Gartner report of 2019, there has been a 270% growth in the applications of AI from 2015 – 2019. Thus, the importance of AI is bound to climb high in the future. AI career opportunities are drastically reaching great heights. However, as per the Gartner Report in 2021, the scope of AI has been increasing rapidly. People are much inclined to do a job in this particular field with the increase in the courses of understanding the principles of AI. Nowadays, 70% of organizations are optimizing AI networks to increase their reach globally at a higher scale. If you want to switch to AI, you should not hesitate and try your hands on it. Moreover, roles associated with AI also pay quite high.  Learn with upGrad AI Courses Numerous benefits of learning AI these days open up doors to ample AI career opportunities. India’s trusted online higher education company, upGrad has come up with its own courses that deal with AI. The Executive PG Program in Machine Learning and AI offered by upGrad is definitely going to assist you in sharpening your AI skills. You can also check IIT Delhi’s Executive PG Programme in Machine Learning  in association with upGrad. IIT Delhi is one of the most prestigious institutions in India. With more the 500+ In-house faculty members which are the best in the subject matters. These two courses are the best-selling courses in the country. You won’t regret taking up any of the courses. The skills you will develop by enrolling in these courses will give an explosive boost to your career in AI.  There are various job opportunities in artificial intelligence one can procure if they take the right steps towards it. The curriculum is suitably designed by top experts in the industry to flourish your career in AI. You don’t have to quit your job and can rather acquire a post-graduate diploma or master’s easily. It also has IIIT Bangalore and Liverpool John Moores University alumni support. Moreover, these courses also give way to AI career opportunities in companies like Microsoft, Uber, PwC, etc. upGrad provides added benefits of outstanding learning support, doubt resolution, and networking. Both these courses are going to kick start your career in AI. 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 conclusion, the landscape of Career Opportunities in Artificial Intelligence (AI) is vast and continually evolving, presenting a plethora of opportunities for mid-career professionals looking to pivot or advance in this dynamic field. Companies across diverse sectors increasingly leverage AI to enhance efficiency, innovation, and decision-making, highlighting AI’s critical role in today’s digital economy. From healthcare to finance and from retail to manufacturing, the applications of AI are broad and varied, offering a wide range of career paths for individuals with the right skill set. As we look to the future, the demand for skilled professionals in AI is only set to increase, underscoring the importance of staying abreast of the latest technologies and trends in this space. For those interested in seizing Career Opportunities in AI, now is the opportune time to delve into this exciting and rewarding field, where the potential for growth and innovation is boundless.  If you’re interested to learn more about machine learning & AI, 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. Refer to your Network! 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As you start learning about supervised learning, it’s important to get acquainted with the concept of decision trees. Decision trees are akin to simplified diagrams that assist in solving various types of problems by making sequential decisions. One key metric used in enhancing the efficiency of decision trees is the Gini Index. This criterion plays a crucial role in guiding decision trees on how to optimally partition the data they’re presented with. Here, we’re looking closely at something called the Decision tree for Gini Index. It’s a tool that helps decision trees decide how to split up the information they’re given.  In this article, I’ll explain the Gini Index in easy words. We’ll talk about perfect and imperfect splits using examples you can relate to. By the end, you’ll see how decision trees can help solve real problems, making it easier for you to use them in your own work. Let’s get started!  What is Gini Index? The Gini Index is a way of quantifying how messy or clean a dataset is, especially when we use decision trees to classify it. It goes from 0 (cleanest, all data points have the same label) to 1 (messiest, data points are split evenly among all labels).  Think of a dataset that shows how much money people make. A high Gini Index for this data means that there is a huge difference between the rich and the poor, while a low Gini Index means that the income is more balanced.  When we build decision trees, we want to use the Gini Index to find the best feature to split the data at each node. The best feature is the one that reduces the Gini Index the most, meaning that it creates the purest child nodes. This way, we can create a tree that can distinguish different labels based on the features.  What Does a Decision Tree do? A decision tree is a machine learning algorithm used for both classification and regression tasks. It resembles a tree-like structure with branches and leaves. Each branch represents a decision based on a specific feature of the data, and the leaves represent the predicted outcome.  Data points navigate through the decision tree based on their respective feature values, traversing down branches determined by the split conditions that are chosen using the decision tree Gini index as a criterion for selection. Ultimately, they reach a leaf and receive the prediction assigned to that leaf. Decision trees are popular for their interpretability and simplicity, allowing easy visualization of the decision-making process. The Gini Index plays a crucial role in building an effective decision tree by guiding the selection of optimal splitting features. By minimizing the Gini index for decision tree at each node, the tree progressively separates data points belonging to different classes, leading to accurate predictions at the terminal leaves.  Here’s a breakdown of how to build decision tree using Gini index:  Calculate the Gini Index of the entire dataset. This represents the initial level of impurity before any splitting.  Consider each feature and its threshold values. For each combination, calculate the Gini Index of the two resulting child nodes after splitting the data based on that feature and threshold.  Choose the feature and threshold combination that leads to the smallest Gini Index for the child nodes. This indicates the most significant decrease in impurity, resulting in a more homogeneous separation of data points.  Repeat the process recursively on each child node. Use the same approach to select the next split feature and threshold, further minimizing the Gini Index and separating data points based on their class labels.  Continue splitting until a stopping criterion is met. This could be reaching a pre-defined tree depth, minimum data size per node, or a sufficiently low Gini Index at all terminal leaves.   By iteratively using the Decision Tree Gini Index to guide feature selection and data partitioning, decision trees can effectively learn complex relationships within the data and make accurate predictions for unseen instances.  Flow of a Decision Tree  Here I have noted the flow of a decision tree Gini index: Training: The decision tree is built by applying a splitting algorithm to the training data. The algorithm chooses the feature and its threshold value that best minimizes the Gini Index within the resulting child nodes. This process is repeated recursively on each subgroup until reaching a stopping criterion, like minimum data size or maximum tree depth.  Prediction: A new data point traverses the tree based on its own feature values, navigating down branches determined by the splitting conditions. Finally, it reaches a leaf and receives the prediction assigned to that leaf.  Ensembles: Decision trees can be combined into ensembles like random forests or boosting to improve accuracy and reduce overfitting. This involves building multiple trees from different subsets of the data and aggregating their predictions, leading to a more robust model.  Calculation The Gini Index or Gini Impurity is calculated by subtracting the sum of the squared probabilities of each class from one. It favours mostly the larger partitions and are very simple to implement. In simple terms, it calculates the probability of a certain randomly selected feature that was classified incorrectly. The Gini Index varies between 0 and 1, where 0 represents purity of the classification and 1 denotes random distribution of elements among various classes. A Gini Index of 0.5 shows that there is equal distribution of elements across some classes. Mathematically, The Gini Index is represented by  The Gini Index works on categorical variables and gives the results in terms of “success” or “failure” and hence performs only binary split. It isn’t computationally intensive as its counterpart – Information Gain. From the Gini Index, the value of another parameter named Gini Gain is calculated whose value is maximized with each iteration by the Decision Tree to get the perfect CART FYI: Free NLP course! Let us understand the calculation of the Gini Index with a simple example. In this, we have a total of 10 data points with two variables, the reds and the blues. The X and Y axes are numbered with spaces of 100 between each term. From the given Gini index Decision tree example , we shall calculate the Gini Index and the Gini Gain. For a decision tree, we need to split the dataset into two branches. Consider the following data points with 5 Reds and 5 Blues marked on the X-Y plane. Suppose we make a binary split at X=200, then we will have a perfect split as shown below. It is seen that the split is correctly performed and we are left with two branches each with 5 reds (left branch) and 5 blues (right branch). But what will be the outcome if we make the split at X=250? We are left with two branches, the left branch consisting of 5 reds and 1 blue, while the right branch consists of 4 blues. The following is referred to as an imperfect split. In training the Decision Tree model, to quantify the amount of imperfectness of the split, we can use the Gini Index.  Checkout: Types of Binary Tree Basic Mechanism To calculate the Gini Impurity, let us first understand it’s basic mechanism. First, we shall randomly pick up any data point from the dataset Then, we will classify it randomly according to the class distribution in the given dataset. In our dataset, we shall give a data point chosen with a probability of 5/10 for red and 5/10 for blue as there are five data points of each colour and hence the probability. Now, in order to calculate the Gini index decision tree formula: Where, C is the total number of classes and p(i) is the probability of picking the data point with the class i. In the above Gini index decision tree solved example, we have C=2 and p(1) = p(2) = 0.5, Hence the Gini Index can be calculated as, G =p(1) ∗ (1−p(1)) + p(2) ∗ (1−p(2))     =0.5 ∗ (1−0.5) + 0.5 ∗ (1−0.5)     =0.5 Where 0.5 is the total probability of classifying a data point imperfectly and hence is exactly 50%. Now, let us calculate the Gini Impurity for both the perfect and imperfect split that we performed earlier, Perfect Split The left branch has only reds and hence its Gini Impurity is, G(left) =1 ∗ (1−1) + 0 ∗ (1−0) = 0 The right branch also has only blues and hence its Gini Impurity is also given by, G(right) =1 ∗ (1−1) + 0 ∗ (1−0) = 0 From the quick calculation, we see that both the left and right branches of our perfect split have probabilities of 0 and hence is indeed perfect. A Gini Impurity of 0 is the lowest and the best possible impurity for any data set. 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 Imperfect Split  In this case, the left branch has 5 reds and 1 blue. Its Gini Impurity can be given by, G(left) =1/6 ∗ (1−1/6) + 5/6 ∗ (1−5/6) = 0.278 The right branch has all blues and hence as calculated above its Gini Impurity is given by, G(right) =1 ∗ (1−1) + 0 ∗ (1−0) = 0 Now that we have the Gini Impurities of the imperfect split, in order to evaluate the quality or extent of the split, we will give a specific weight to the impurity of each branch with the number of elements it has. (0.6∗0.278) + (0.4∗0) = 0.167 Now that we have calculated the Gini Index, we shall calculate the value of another parameter, Gini Gain and analyse its application in Decision Trees. The amount of impurity removed with this split is calculated by deducting the above value with the Gini Index for the entire dataset (0.5) 0.5 – 0.167 = 0.333 This value calculated is called as the “Gini Gain”. In simple terms, Higher Gini Gain = Better Split.  Hence, in a Decision Tree algorithm, the best split is obtained by maximizing the Gini Gain, which is calculated in the above manner with each iteration.  After calculating the Gini Gain for each attribute in the data set, the class, sklearn.tree.DecisionTreeClassifier will choose the largest Gini Gain as the Root Node. When a branch with Gini of 0 is encountered it becomes the leaf node and the other branches with Gini more than 0 need further splitting. These nodes are grown recursively till all of them are classified. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Also Read: Decision Tree in AI: Introduction, Types & Creation Relevance of Entropy Entropy, a key concept in decision trees, measures the uncertainty or randomness within a dataset. It specifically quantifies the degree to which a subset of data contains examples belonging to different classes, playing a crucial role in the decision-making process of the tree. By choosing features that minimize entropy within splits, we lead to purer branches and, ultimately, construct a more accurate decision tree. While both the Gini Index and entropy are utilized in decision trees to assess data purity, they calculate the difference in impurity slightly differently. The Gini Index, like entropy, serves as a metric to evaluate the likelihood of a specific feature being misclassified when selected randomly. However, entropy in the decision tree gives a more detailed measure of the disorder or variability of the system, offering a slightly different perspective on data purity and impurity reduction strategies. Gini Index: Compares the proportion of each class within a data subset before and after the split, favoring features that maximize the difference.  Entropy: Compares the overall uncertainty of the original data to the combined uncertainty of the resulting subsets, preferring features that lead to the largest decrease in overall entropy.  Both Gini Index and entropy have their advantages and disadvantages, and the choice depends on the specific data and task. Generally, Gini Index works well for binary classification, while entropy might be better suited for multiple classes.  Difference between Gini Index and Entropy Factor Gini Index Entropy Definition Measures the probability of misclassification. Measures the amount of information (or uncertainty) in a dataset. Formula Gini=1−∑i=1n​pi2​ Entropy=−∑i=1n​pi​log2​(pi​) Range 0 to 0.5 for binary classification. 0 to 1 for binary classification. Impurity Lower values indicate purer nodes. Lower values indicate purer nodes. Calculation Complexity Generally simpler to compute. Generally more complex to compute. Splitting Criterion Prefers to maximize the probability of a single class. Prefers splits that create the most uniform class distribution. Use in Algorithms Commonly used in the CART (Classification and Regression Tree) algorithm. Commonly used in the ID3 (Iterative Dichotomiser 3) and C4.5 algorithms. Sensitivity to Data Distribution Less sensitive to changes in class distribution. More sensitive to changes in class distribution. Interpretation Measures how often a randomly chosen element would be incorrectly classified. Measures the average amount of information required to identify the class of an element. Bias Towards Purity Slightly biased towards larger classes. More balanced, less biased towards larger or smaller classes. Behavior at Pure Nodes At a pure node (one class), Gini = 0. At a pure node (one class), Entropy = 0. Mathematical Nature Quadratic measure. Logarithmic measure. Robustness to Outliers More robust to outliers due to its quadratic nature. Less robust to outliers due to the logarithmic calculation. Preferred When Simplicity and speed are crucial. A more nuanced measure of information gain is needed. Gini Index vs Information Gain Both Gini Index and Information Gain are measures of impurity used in decision trees to choose the best feature for splitting the data at each node. However, they calculate this difference in slightly different ways and have their own strengths and weaknesses.  Gini Index:  Focuses on class proportions: Compares the proportion of each class within a data subset before and after the split, favoring features that maximize the difference. This makes it sensitive to class imbalance, potentially favoring splits that isolate minority classes even if they don’t significantly improve overall clarity.  Simple and computationally efficient: Easier to calculate compared to Information Gain, making it faster to build decision trees.  Works well for binary classification: Emphasizes maximizing the gap between classes, making it effective when dealing with two distinct outcomes.  Information Gain:  Measures entropy change: Compares the total entropy of the original data to the combined entropy of the resulting subsets after the split, preferring features that lead to the largest decrease in overall uncertainty. This is more nuanced and can handle multiple classes effectively.  Less sensitive to class imbalance: Doesn’t solely focus on isolating minority classes but accounts for overall reduction in uncertainty even if the split proportions are uneven.  More computationally expensive: Calculating entropy involves logarithms, making it slightly slower than Gini Index for tree construction.  Can be better for multi-class problems: Provides a more comprehensive picture of class distribution changes, potentially leading to better results with multiple outcomes.  Here’s a table summarizing the key differences:  Feature  Gini Index  Information Gain  Focus  Class proportions  Entropy change  Strengths  Simple, efficient, good for binary classification  More nuanced, handles imbalance, good for multiple classes  Weaknesses  Sensitive to class imbalance, less informative for multiple classes.  More computationally expensive    Use in Machine Learning There are various algorithms designed for different purposes in the world of machine learning. The problem lies in identifying which algorithm to suit best on a given dataset. The decision tree algorithm seems to show convincing results too. To recognize it, one must think that decision trees somewhat mimic human subjective power. So, a problem with more human cognitive questioning is likely to be more suited for decision trees. The underlying concept of decision trees can be easily understandable for its tree-like structure.  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 An alternative to the Decision tree for Gini Index is the Information Entropy which used to determine which attribute gives us the maximum information about a class. It is based on the concept of entropy, which is the degree of impurity or uncertainty. It aims to decrease the level of entropy from the root nodes to the leaf nodes of the decision tree.  In this way, the Gini Index is used by the CART algorithms to optimise the decision trees and create decision points for classification trees.  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.

Recent advancements have paved the growth of multiple algorithms. These new and blazing algorithms have set the data on fire. They help in handling data and making decisions with them effectively. Since the world is dealing with an internet spree. Almost everything is on the internet. To handle such data, we need rigorous algorithms to make decisions and interpretations. Now, in the presence of a wide list of algorithms, it’s a hefty task to choose the best suited.  Have you ever heard the terms decision tree random forest? If not, then keep on reading to get a detailed insight on decision tree random forest and learn how they are different from each other. The following article will also shed some light on the advantages of random forest over decision tree.  Decision-making algorithms are widely used by most organizations. They have to make trivial and big decisions every other hour. From analyzing which material to choose to get high gross areas, a decision is happening in the backend. The recent python and ML advancements have pushed the bar for handling data. Thus, data is present in huge bulks. The threshold depends on the organization. There are 2 major decision algorithms widely used. Decision Tree and Random Forest- Sounds familiar, right? Trees and forests!  Let’s explore this with an easy example. Suppose you have to buy a packet of Rs. 10 sweet biscuits. Now, you have to decide one among several biscuits’ brands.  You choose a decision tree algorithm. Now, it will check the Rs. 10 packet, which is sweet. It will choose probably the most sold biscuits. You will decide to go for Rs. 10 chocolate biscuits. You are happy! But your friend used the Random forest algorithm. Now, he has made several decisions. Further, choosing the majority decision. He chooses among various strawberry, vanilla, blueberry, and orange flavors. He checks that a particular Rs. 10 packet served 3 units more than the original one. It was served in vanilla chocolate. He bought that vanilla choco biscuit. He is the happiest, while you are left to regret your decision. Join the Machine Learning Course from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. What is the difference between the Decision Tree and Random Forest? 1. Decision Tree Source Decision Tree is a supervised learning algorithm used in machine learning. It operated in both classification and regression algorithms. As the name suggests, it is like a tree with nodes. The branches depend on the number of criteria. It splits data into branches like these till it achieves a threshold unit. A decision tree has root nodes, children nodes, and leaf nodes. Recursion is used for traversing through the nodes. You need no other algorithm. It handles data accurately and works best for a linear pattern. It handles large data easily and takes less time. How does it work? 1. Splitting Data, when provided to the decision tree, undergoes splitting into various categories under branches.  Must Read: Naive Bayes Classifier: Pros & Cons, Applications & Types Explained 2. Pruning Pruning is shredding of those branches furthermore. It works as a classification to subsidize the data in a better way. Like, the same way we say pruning of excess parts, it works the same. The leaf node is reached, and pruning ends. It’s a very important part of decision trees. 3. Selection of trees Now, you have to choose the best tree that can work with your data smoothly. Here are the factors that need to be considered:  4. Entropy  To check the homogeneity of trees, entropy needs to be inferred. If the entropy is zero, it’s homogenous; else not. 5. Knowledge gain Once the entropy is decreased, the information is gained. This information helps to split the branches further. You need to calculate the entropy. Split the data on the basis of different criteria Choose the best information. Tree depth is an important aspect. The depth informs us of the number of decisions one needs to make before we come up with a conclusion. Shallow depth trees perform better with decision tree algorithms.  Must Read: Free nlp online course! Advantages and Disadvantages of Decision Tree The list mentioned below highlights the major strengths and weaknesses of decision tree. Advantages Easy Transparent process Handle both numerical and categorical data Larger the data, the better the result Speed  Can generate understandable rules. Has the ability to perform classification without the need for much computation. Gives a clear indication of the most important fields for classification or prediction. Disadvantages May overfit Pruning process large Optimization unguaranteed Complex calculations Deflection high Can be less appropriate for estimation tasks, especially in cases where the ultimate aim is to determine a continuous attribute’s value.  Are more prone to errors in classification problems  Can be computationally expensive to train.  Checkout: Machine Learning Models Explained 2. Random Forest Source What is Random Forest? Random Forest is yet another very popular supervised machine learning algorithm that is used in classification and regression problems. One of the main features of this algorithm is that it can handle a dataset that contains continuous variables, in the case of regression. Simultaneously, it can also handle datasets containing categorical variables, in the case of classification. This in turn helps to deliver better results for classification problems.  It is also used for supervised learning but is very powerful. It is very widely used. The basic difference being it does not rely on a singular decision. It assembles randomized decisions based on several decisions and makes the final decision based on the majority. It does not search for the best prediction. Instead, it makes multiple random predictions. Thus, more diversity is attached, and prediction becomes much smoother. 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 You can infer Random forest to be a collection of multiple decision trees! Bagging is the process of establishing random forests while decisions work parallelly. 1. Bagging Take some training data set Make a decision tree Repeat the process for a definite period Now take the major vote. The one that wins is your decision to take. 2. Bootstrapping Bootstrapping is randomly choosing samples from training data. This is a random procedure.  STEP by STEP Random choose conditions Calculate the root node Split Repeat You get a forest Read : Naive Bayes Explained In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Advantages and Disadvantages of Random Forest Advantages Powerful and highly accurate No need to normalizing Can handle several features at once Run trees in parallel ways Can perform both regression and classification tasks. Produces good prediction that is easily understandable. Disadvantages They are biased to certain features sometimes Slow- One of the major disadvantages of random forest is that due to the presence of a large number of trees, the algorithm can become quite slow and ineffective for real-time predictions.  Can not be used for linear methods Worse for high dimensional data Since the random forest is a predictive modeling tool and not a descriptive one, it would be better to opt for other methods, especially if you are trying to find out the description of the relationships in your data.  Difference between random forest and decision tree: Feature Decision Tree Random Forest Basic Structure Single tree Ensemble of multiple trees Training Typically faster Slower due to training multiple trees Bias-Variance Tradeoff Prone to overfitting Reduces overfitting by averaging predictions Performance Can suffer from high variance More robust due to averaging predictions Prediction Speed Faster Slower due to multiple predictions Interpretability Easier to interpret More difficult to interpret due to complexity Handling Outliers Sensitive (can overfit) Less sensitive due to averaging Feature Importance Can rank features Can rank features based on importance Data Requirements Works well with small to moderate datasets Can handle large datasets better Parallelization Not easily parallelizable Easily parallelizable training Application Often used as a base model Often used when higher accuracy is required What are some of the important features of Random Forest? Now that you have a basic understanding of the difference between random forest decision tree, let’s take a look at some of the important features of random forest that sets it apart. The following random forest decision tree list will also highlight some of the advantages of random forest over decision tree.  Diversity-  Each tree is different, and does not consider all the features. This means that not all features and attributes are considered while making an individual tree.  Parallelization – You get to make full use of the CPU to build random forests. The reason behind this being each tree is created out of different data and attributes, independently.  Stability- Random forest ensures full stability since the result is based on majority voting or averaging.  Train-test Split- Last but not least, yet another important feature of random forest is that you don’t have to separate the data for train and test since 30% of the data unseen by the decision tree is always available.  When exploring random forest vs decision tree python implementations, decision trees offer simplicity and quick setup, while random forests enhance accuracy and robustness by averaging multiple trees. For a clear random forest vs decision tree example, consider a classification task: a decision tree might quickly classify data but risks overfitting, while a random forest combines multiple trees to improve accuracy and reduce overfitting. 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 Decision trees are very easy as compared to the random forest. A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training. When you are trying to put up a project, you might need more than one model. Thus, a large number of random forests, more the time.  It depends on your requirements. If you have less time to work on a model, you are bound to choose a decision tree. However, stability and reliable predictions are in the basket of random forests.  If you have the passion and want to learn more about artificial intelligence, you can take up IIIT-B & upGrad’s PG Diploma in Machine Learning and Deep Learning that offers 400+ hours of learning, practical sessions, job assistance, and much more.

Introduction In the last few years of the IT industry, there has been a huge demand for once particular skill set known as Deep Learning. Deep Learning a subset of Machine Learning which consists of algorithms that are inspired by the functioning of the human brain or the neural networks. Check out our free data science courses to get an edge over the competition. These structures are called as Neural Networks. It teaches the computer to do what naturally comes to humans. Deep learning, there are several types of models such as the Artificial Neural Networks (ANN), Autoencoders, Recurrent Neural Networks (RNN) and Reinforcement Learning. But there has been one particular model that has contributed a lot in the field of computer vision and image analysis which is the Convolutional Neural Networks (CNN) or the ConvNets.  CNN is very useful as it minimises human effort by automatically detecting the features. For example, for apples and mangoes, it would automatically detect the distinct features of each class on its own. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. CNNs are a class of Deep Neural Networks that can recognize and classify particular features from images and are widely used for analyzing visual images. Their applications range from image and video recognition, image classification, medical image analysis, computer vision and natural language processing. CNN has high accuracy, and because of the same, it is useful in image recognition. Image recognition has a wide range of uses in various industries such as medical image analysis,  phone, security, recommendation systems, etc.  The term ‘Convolution” in CNN denotes the mathematical function of convolution which is a special kind of linear operation wherein two functions are multiplied to produce a third function which expresses how the shape of one function is modified by the other. In simple terms, two images which can be represented as matrices are multiplied to give an output that is used to extract features from the image. Learn Machine Learning online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Basics of CNN Architecture Convolutional Neural Networks (CNNs) are deep learning models that extract features from images using convolutional layers, followed by pooling and fully connected layers for tasks like image classification. They excel in capturing spatial hierarchies and patterns, making them ideal for analyzing visual data. There are two main parts to a CNN architecture A convolution tool that separates and identifies the various features of the image for analysis in a process called as Feature Extraction.  The network of feature extraction consists of many pairs of convolutional or pooling layers.  A fully connected layer that utilizes the output from the convolution process and predicts the class of the image based on the features extracted in previous stages. This CNN model of feature extraction aims to reduce the number of features present in a dataset. It creates new features which summarises the existing features contained in an original set of features. There are many CNN layers as shown in the basic CNN architecture with diagram. Source   Featured Program for you: Fullstack Development Bootcamp Course There are three types of CNN architecture which are the convolutional layers, pooling layers, and fully-connected (FC) layers. When these layers are stacked, a CNN architecture will be formed. In addition to these three layers, there are two more important parameters which are the dropout layer and the activation function which are defined below. Good Read: Introduction to Deep Learning & Neural Networks 1. Convolutional Layer This layer is the first layer that is used to extract the various features from the input images. In this layer, the mathematical operation of convolution is performed between the input image and a filter of a particular size MxM. By sliding the filter over the input image, the dot product is taken between the filter and the parts of the input image with respect to the size of the filter (MxM). The output is termed as the Feature map which gives us information about the image such as the corners and edges. Later, this feature map is fed to other layers to learn several other features of the input image. The convolution layer in CNN passes the result to the next layer once applying the convolution operation in the input. Convolutional layers in CNN benefit a lot as they ensure the spatial relationship between the pixels is intact. 2. Pooling Layer In most cases, a Convolutional Layer is followed by a Pooling Layer. The primary aim of this layer is to decrease the size of the convolved feature map to reduce the computational costs. This is performed by decreasing the connections between layers and independently operates on each feature map. Depending upon method used, there are several types of Pooling operations. It basically summarises the features generated by a convolution layer. In Max Pooling, the largest element is taken from feature map. Average Pooling calculates the average of the elements in a predefined sized Image section. The total sum of the elements in the predefined section is computed in Sum Pooling. The Pooling Layer usually serves as a bridge between the Convolutional Layer and the FC Layer. This CNN model generalises the features extracted by the convolution layer, and helps the networks to recognise the features independently. With the help of this, the computations are also reduced in a network. Must Read: Neural Network Project Ideas 3. Fully Connected Layer The Fully Connected (FC) layer consists of the weights and biases along with the neurons and is used to connect the neurons between two different layers. These layers are usually placed before the output layer and form the last few layers of a CNN Architecture. In this, the input image from the previous layers are flattened and fed to the FC layer. The flattened vector then undergoes few more FC layers where the mathematical functions operations usually take place. In this stage, the classification process begins to take place. The reason two layers are connected is that two fully connected layers will perform better than a single connected layer. These layers in CNN reduce the human supervision 4. Dropout Usually, when all the features are connected to the FC layer, it can cause overfitting in the training dataset. Overfitting occurs when a particular model works so well on the training data causing a negative impact in the model’s performance when used on a new data. To overcome this problem, a dropout layer is utilised wherein a few neurons are dropped from the neural network during training process resulting in reduced size of the model. On passing a dropout of 0.3, 30% of the nodes are dropped out randomly from the neural network. Dropout results in improving the performance of a machine learning model as it prevents overfitting by making the network simpler. It drops neurons from the neural networks during training. Must Read: Free deep learning course! 5. Activation Functions Finally, one of the most important parameters of the CNN model is the activation function. They are used to learn and approximate any kind of continuous and complex relationship between variables of the network. In simple words, it decides which information of the model should fire in the forward direction and which ones should not at the end of the network. It adds non-linearity to the network. There are several commonly used activation functions such as the ReLU, Softmax, tanH and the Sigmoid functions. Each of these functions have a specific usage. For a binary classification CNN model, sigmoid and softmax functions are preferred an for a multi-class classification, generally softmax us used. In simple terms, activation functions in a CNN model determine whether a neuron should be activated or not. It decides whether the input to the work is important or not to predict using mathematical operations. Importance of ReLU in CNN ReLU (Rectified Linear Unit) is a popular activation function used in Convolutional Neural Networks (CNNs). It introduces non-linearity by outputting the input directly if it’s positive and zero otherwise, helping models to learn complex patterns. 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 LeNet-5 CNN Architecture  In 1998, the LeNet-5 architecture was introduced in a research paper titled “Gradient-Based Learning Applied to Document Recognition” by Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. It is one of the earliest and most basic CNN architecture. It consists of 7 layers. The first layer consists of an input image with dimensions of 32×32. It is convolved with 6 filters of size 5×5 resulting in dimension of 28x28x6. The second layer is a Pooling operation which filter size 2×2 and stride of 2. Hence the resulting image dimension will be 14x14x6. Similarly, the third layer also involves in a convolution operation with 16 filters of size 5×5 followed by a fourth pooling layer with similar filter size of 2×2 and stride of 2. Thus, the resulting image dimension will be reduced to 5x5x16. Once the image dimension is reduced, the fifth layer is a fully connected convolutional layer with 120 filters each of size 5×5. In this layer, each of the 120 units in this layer will be connected to the 400 (5x5x16) units from the previous layers. The sixth layer is also a fully connected layer with 84 units. The final seventh layer will be a softmax output layer with ‘n’ possible classes depending upon the number of classes in the dataset. Source Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. The above diagram is a representation of the 7 layers of the LeNet-5 CNN Architecture. Below are the snapshots of the Python code to build a LeNet-5 CNN architecture using keras library with TensorFlow framework In Python Programming, the model type that is most commonly used is the Sequential type. It is the easiest way to build a CNN model in keras. It permits us to build a model layer by layer. The ‘add()’ function is used to add layers to the model. As explained above, for the LeNet-5 architecture, there are two Convolution and Pooling pairs followed by a Flatten layer which is usually used as a connection between Convolution and the Dense layers. The Dense layers are the ones that are mostly used for the output layers. The activation used is the ‘Softmax’ which gives a probability for each class and they sum up totally to 1. The model will make it’s prediction based on the class with highest probability.  The summary of the model is displayed as below. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Conclusion Hence, in this article we have understood the basic CNN structure, it’s architecture and the various layers that make up the CNN model. Also, we have seen basic CNN architecture example of a very famous and traditional LeNet-5 model with its Python program. We have understood how the dependence on humans decreases to build effective functionalities. Distinct layers in CNN transform the input to output using differentiable functions. If you’re interested to learn more about machine learning courses, 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. 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