Top 22 Artificial Intelligence Project Ideas & Topics for Beginners [2024]

Updated on 19 May, 2024

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Artificial Intelligence Project Ideas & Topics

In this article, you will learn the 22 AI project ideas & Topics. Take a glimpse below.

Best AI Project Ideas & Topics

  1. Predict Housing Price
  2. Enron Investigation
  3. Stock Price Prediction
  4. Customer Recommendation
  5. Chatbots
  6. Voice-based Virtual Assistant for Windows
  7. Facial Emotion Recognition and Detection
  8. Online Assignment Plagiarism Checker
  9. Personality Prediction System via CV Analysis
  10. Heart Disease Prediction Project
  11. Banking Bot
  12. Differentiate the music genre from an audio file
  13. Image reconstruction by using an occluded scene
  14. Identify human emotions through pictures
  15. Summarize articles written in technical text
  16. Filter the content and identify spam
  17. Fake News Detector
  18. Translator App
  19. Instagram Spam Detection
  20. Objection Detection System
  21. Animal Species Prediction
  22. Image to Pencil Sketch App

Read the full article to know more about all the AI based projects for final year in detail.

Only learning theory is not enough. That’s why everyone encourages students to try artificial intelligence projects and complete them. From following the artificial intelligence trends to getting their hands dirty on projects. So, if you are a beginner, the best thing you can do is work on some real-time Artificial Intelligence project ideas.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Artificial Intelligence project ideas which beginners can work on to put their Python knowledge to test. In this article, you will find 22 top Artificial Intelligence project ideas for beginners to get hands-on experience on AI. 

You may often catch yourself talking to or asking a question to Siri or Alexa, right? Self-driving cars are no longer something you dreamed of or watched in a sci-fi, either, is it? So, how are machines acting and doing things that we thought only humans could?

The simple answer is artificial intelligence or AI. For decades scientists have worked on making AI possible. And today, we have reached a point where we have access to them in our daily lives. It doesn’t matter whether you are navigating the streets with the help of your AI-enabled navigation system or asking for movie recommendations from the comforts of your home- AI has touched all our lives. 

If you read the reports on the future of jobs or the digital transformations today, you will come across several interesting topics in artificial intelligence. Conversations revolving around artificial intelligence topics, such as its impact on our work and life, have become a mainstay in the mainstream media. 

According to data, the global AI market has been valued at US$ 51.08 billion. This number is expected to rise to US$ 641.30 billion by 2028. In fact, the pandemic has been driving investment in AI, with 86% of organizations saying that they have or will invest in AI initiatives. Experts have even predicted that AI-related jobs will increase by 31.4% by 2030.

With such an optimistic outlook, it is not surprising that many are turning to artificial intelligence and machine learning for their future. The career prospects are immense in this field, and exposing yourself to the practical dimensions of artificial intelligence topics is very important. 

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These projects will help you in advancing your skills as an expert while testing your current knowledge at the same time. You can use artificial intelligence in multiple sectors. The more you experiment with different Artificial Intelligence project ideas, the more knowledge you gain.

In this article, we’ll be discussing some of the most exciting artificial intelligence project ideas for beginners:

As beginners, choosing among these AI topics and research ideas for your project may seem daunting.  After all, artificial intelligence topics are very new, and you will read about many interesting topics in artificial intelligence. Reading about the fundamentals of these AI topics is very important, but you have to gain practical know-how to grow in the field. 

You can also consider doing our Python Bootcamp course from upGrad to upskill your career.

What are Artificial Intelligence Projects For Final Year Students?

Artificial Intelligence (AI) projects are initiatives or endeavors that involve applying AI techniques, technologies, and methodologies to solve specific problems or create innovative solutions. These projects leverage the capabilities of AI, such as machine learning, deep learning, natural language processing, computer vision, and more, to automate tasks, make predictions, analyze data, and mimic human-like intelligence.

AI projects vary widely in scope and complexity, ranging from small-scale experimental prototypes to large-scale, enterprise-level systems. They can be applied across various domains and industries, including healthcare, finance, manufacturing, transportation, entertainment, and more.

Why you should do AI-Based Projects

There are many benefits to doing AI projects for students. This topic is extensive and diverse. Moreover, it requires you to have a considerable amount of technical knowledge.

Doing AI-based projects can help you in multiple ways. Here are the main reasons why:

Learning Experience

You get hands-on experience with these projects. You get to try out new stuff and understand how everything works. If you want to learn the real-life application of artificial intelligence, then it’s the best way to do so.

Artificial Intelligence projects cover numerous industries and domains. And unless you complete them yourself, you won’t know what challenges they give. By completing these projects, you will become more proficient with AI as well.

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You will need to acquaint yourself with new tools and technologies while working on a python project. The more you learn about cutting-edge development tools, environments, libraries, the broader will be your scope for experimentation with your projects. The more you experiment with different AI project ideas, the more knowledge you gain.

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Portfolio

After learning AI, you’d surely want to get a job in this field. But how will you showcase your talent?

AI projects can help you in that regard too. They help you show your skills to the recruiters. Each project poses a different challenge, and you can mention them while describing the project.

Apart from that, it also shows that you have experience in applying your AI knowledge in the real-world. There’s a considerable difference between theoretical knowledge and practical knowledge. The artificial intelligence projects for students you would’ve completed will enhance your portfolio.

Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs.

See your Progress

You can find out how much of an AI expert you have become only by completing such projects. These projects require you to use your knowledge of artificial intelligence and its tools in creative ways.

If you want to see how much progress you’ve made as an artificial intelligence expert, you should test your knowledge with these unique and interesting projects.

What are the best Platforms to Work on AI Projects?

1. TensorFlow

  • Introduced by Google, TensorFlow is one of the open-source library for both machine learning and in-depth learning projects.
  • Delivers a flexible ecosystem for creating and training various AI models, including neural networks.
  • Provides tools for beginners and experts and support for deployment on various platforms.

2. PyTorch

  • Backed by Facebook’s Artificial Intelligence Research lab it is another famous and most used open-source framework.
  • Known for its dynamic computation graph, making it more intuitive for research and experimentation.
  • Offers a strong community and extensive documentation, suitable for a wide range of AI projects.

3. Keras

  • Keras is a another highly advanced neural networks API that works on top of various AI platforms like, TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK).
  • Ideal for rapid prototyping due to its easily navigational interface and ease of use.
  • Enables quick experimentation with neural network architectures.

4. Scikit-learn

  • A versatile open-source machine learning library that provides simple and efficient tools for data mining and data analysis.
  • Well-suited for classical machine learning algorithms, including classification, regression, clustering, and more.
  • Integrates well with other scientific Python libraries.

5. Microsoft Azure ML

  • Microsoft’s cloud-based machine learning platform offers tools for building, training, and deploying AI models.
  • Provides a drag-and-drop interface for beginners and advanced capabilities for data scientists.
  • Offers integration with other Azure services for seamless deployment.

6. Google Cloud AI Platform

  • This platform supports end-to-end AI model development as part of the Google Cloud ecosystem.
  • Provides managed services for training and deploying machine learning models at scale.
  • Offers integration with TensorFlow and scikit-learn.

7. Amazon SageMaker

  • Amazon’s machine learning platform simplifies the process of building, training, and deploying models.
  • Supports various popular frameworks and algorithms, along with tools for data preprocessing.
  • Seamlessly integrates with Amazon Web Services (AWS) for scalable deployment.

8. IBM Watson

  • IBM’s AI platform offers tools and services for building and deploying AI applications.
  • Supports natural language processing, computer vision, and data analytics.
  • Provides APIs for incorporating AI capabilities into applications.

9. H2O.ai

  • H2O.ai offers an open-source platform for scalable machine learning and deep learning.
  • Suitable for data scientists and engineers to develop AI models with a focus on scalability and performance.
  • Provides automated machine learning (AutoML) features for streamlined model building.

10. FastAI

  • FastAI is a deep learning library that simplifies training high-quality models.
  • Offers pre-built architectures and techniques for tasks like image classification and natural language processing.
  • Designed to make deep learning more accessible and practical for beginners.

These platforms offer a range of tools and services to cater to different skill levels and project requirements. Your choice of platform should depend on factors like your familiarity with the tools, the complexity of your project, and any specific integration needs with other technologies or services.

So, here are a few Artificial Intelligence Project ideas which beginners can work on:

Top Artificial Intelligence Project Ideas For College Students – Basic & Intermediate Level

This list of simple AI projects ideas for students is suited for beginners, and those just starting out with AI. These AI project ideas will get you going with all the practicalities you need to succeed in your career as a AI Engineer.

Further, if you’re looking for Artificial Intelligence project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some Artificial Intelligence project ideas that will strengthen your base and allow you to climb up the ladder.

Finding artificial intelligence project ideas for students can be tricky. That’s why we have assorted the following list of the same:

1. Predict Housing Price

Just getting into our first Artificial Intelligence Project Ideas. In this project, you will have to predict the selling price of a new home in Boston. The dataset of this project contains the prices of houses in different areas of the city. You can get the datasets for this project at the UCI Machine Learning Repository.

Apart from the prices of various homes, you will get additional datasets containing the age of the residents, the crime rate in the city, and locations of non-retail businesses. For beginners, it’s a great project to test your knowledge. 

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2. Enron Investigation

Enron was one of the biggest energy companies at a time in the US, but it collapsed in 2000 because of a significant allegation of fraud. It was a massive scandal in American history.

Enron might have gone, but its database hasn’t. The database we’re talking about is its email database, which has around 500,000 emails between its former employees and executives. All the emails in the database are real, so this project gets more interesting.

You can use this database for social network analysis (building graph models to find influencers) or anomaly detection (find abnormal behavior by mapping the distribution of sent emails). This is one of the popular AI projects. 

This project is quite popular among data scientists, so don’t hesitate to ask a question in the community.

You can get the data for this project here.

3. Stock Price Prediction

This is one of the excellent Artificial Intelligence project ideas for beginners. ML experts love the share market. And that’s because it’s filled with data. You can get different kinds of data sets and start working on a project right away.

Students who are planning to work in the finance sector would love this project as it can help them get a great insight into different sections of the same. The feedback cycles of the stock market are also short, so it helps in validating your predictions.

You can try to predict 6-month price movements of a stock by using the data you get from the organization’s provided reports in this AI project. 

4. Customer Recommendation

E-commerce has benefitted dramatically from AI. The finest example is Amazon and its customer recommendation system. This customer recommendation system has helped the platform in enhancing its income tremendously thanks to better customer experience.

You can try to build a customer recommendation system for an E-commerce platform, as well. You can use the browsing history of the customer for your data.

5. Chatbots

One of the best AI-based projects is to create a chatbot. You should start by creating a basic chatbot for customer service. You can take inspiration from the chatbots present on various websites. Once you’ve created a simple chatbot, you can improve it and create a more detailed version of the same.

You can then switch up the niche of the chatbot and enhance its functions. There are many new chatbots you can create using AI. Click to learn more if you are interested to learn about creating chatbot in python. 

Artificial IntelligenceProject Ideas – Advanced Level

6. Voice-based Virtual Assistant for Windows

This is one of the interesting Artificial Intelligence project ideas. Voice-based personal assistants are handy tools for simplifying everyday tasks. For instance, you can use virtual voice assistants to search for items/services on the Web, to shop for products for you, to write notes and set reminders, and so much more. 

This voice-based virtual assistant is specially designed for Windows. A Windows user can use this assistant to open any application (Notepad, File Explorer, Google Chrome, etc.) they want by using voice command – “open.” You can also write important messages using the “write” voice command.

Similarly, the voice command for searching the Web is “search.” The NLP trained assistant is trained to understand natural human language, so it will hear the speech and save the command in the database. It will identify a user’s intent from the spoken word and perform the actions accordingly. It can convert text to speech as well. 

7. Facial Emotion Recognition and Detection

This is one of the trending artificial intelligence project ideas. This project seeks to expand on a pioneering modern application of Deep Learning – facial emotion recognition. Although facial emotion recognition has long been the subject of research and study, it is only now that we are witnessing tangible results of that analysis.

The Deep Learning facial emotion detection and recognition system are designed to identify and interpret human facial expressions. It can detect the core human emotions in real-time, including happy, sad, angry, afraid, surprise, disgust, and neutral. First, the automatic facial expression recognition system will detect the facial expressions from a cluttered scene to perform facial feature extraction and facial expression classification.

Then, it will enforce a Convolution Neural Network (CNN) for training a dataset (FER2013). This dataset contains seven facial features – happy, sad, surprise, fear, anger, disgust, and neutral. The unique aspect of this facial emotion detection and recognition system is that it can monitor human emotions, discriminate between good and bad emotions, and label them appropriately. It can also use the tagged emotion information to identify the thinking patterns and behavior of a person.

8. Online Assignment Plagiarism Checker

This is one of the needed AI projects of the hour. Plagiarism is a serious issue that needs to be controlled and monitored. It refers to the act of blindly copying someone else’s work and presenting it as your unique work. Plagiarism is done by paraphrasing sentences, using similar keywords, changing the form of sentences, and so on. In this sense, plagiarism is like theft of intellectual property. 

In this project, you will develop a plagiarism detector that can detect the similarities in copies of text and detect the percentage of plagiarism. This plagiarism detector used the text mining method. In this software, users can register by login by creating a valid login id and password.

So, you can log in using your unique ID and password and upload your assignment file. After the upload is complete, the file will be divided into content and reference link. The checker will then process the full content, check grammatical errors, visit each reference link, and scan the content of all the links to find matches with your content. Users can also store their files and view them later. 

9. Personality Prediction System via CV Analysis

This is one of the interesting Artificial Intelligence project ideas. It is a challenging task to shortlisting deserving candidates from a massive pile of CVs. What if there’s a software that can interpret the personality of a candidate by analyzing their CV? This will make the selection process much more manageable. This project aims to create advanced software that can provide a legally justified and fair CV ranking system. 

The system will work something like this – candidates will register in the system by entering all the relevant details and upload their CV. They will also take an online test that focuses on personality traits and a candidate’s aptitude. Candidates can also view their test results. 

First, the system will rank candidates based on their skills and experience for a particular job profile. It will also consider all other crucial aspects, like soft skills, interests, professional certifications, etc. This will eliminate all the unsuitable candidates for a job role and create a list of the most suitable candidates for the same. Together with the online personality test and CV analysis, the system will create a comprehensive picture of the candidates, simplifying the HR department’s job. 

10. Heart Disease Prediction Project

This project is beneficial from the medical perspective since it is designed to provide online medical consultation and guidance to patients suffering from heart diseases. Patients often complain that they cannot find good doctors to support their medical needs, which further aggravates their situation. This heart disease prediction application will help combat the issue. 

The proposed online application will allow patients (users) to get instant access to the consultation and services of certified medical professionals on matters related to heart diseases. The application will be trained and fed with the details of a wide range of different heart diseases. Users can share and mention their heart-related issues on the online portal.

The system will then process that information to check the database for various possible illnesses associated with those specific details. This intelligent system uses data mining techniques to guess the most accurate disease that could be associated with the details provided by a patient. Users can then consult specialist doctors based on the diagnosis of the system. The system allows users to view the details of different doctors as well. 

11. Banking Bot 

This is one of the excellent Artificial Intelligence project ideas for beginners. This AI project involves building a banking bot that uses artificial intelligence algorithms that analyze user queries to understand their message and accordingly perform the appropriate action. It is a specially designed application for banks where users can ask for bank-related questions like account, loan, credit cards, etc. If you are looking for a good AI projects to add to your resume, this is the one. 

The banking bot is an Android application. Like a chatbot, it is trained to process the users’ queries/requests and understand what services or information they are looking for. The bot will communicate with users like another human being. So, no matter how you ask a question, the bot can answer it and, if required, even escalate issues to human executives. 

Artificial Intelligence Project Ideas – Additional Topics

When you complete the projects mentioned above, you can start working on some of the other topics for AI projects mentioned below:

12. Differentiate the music genre from an audio file

13. Image reconstruction by using an occluded scene

14. Identify human emotions through pictures

15. Summarize articles written in technical text

16. Filter the content and identify spam

Other Interesting AI Projects

You can also check some other ideas for AI projects or AI based projects where professionals can show their expertise:

17. Fake News Detector

The fast-spreading nature of fraudulent information regards to AI project ideas has emerged as a pressing issue. Distorted facts, cleverly disguised as authentic news, can easily deceive and mislead. In particularly crucial moments, such as political elections or global pandemics, the insidious impact of fake news becomes amplified.

The rapid spread of rumors and deceitful reports of AI project ideas can have severe consequences, even endangering human lives. In light of this, it is imperative to promptly detect and combat this phenomenon to prevent the escalation of panic and the misguidance of a vast population. This presents an opportunity for an interesting AI projects or artificial intelligence projects for final year.

Your mission is to develop a fabricated news identifier by utilizing the Real and Fake News dataset from Kaggle. For an added dose of excitement, you have the option to incorporate the top-of-the-line BERT model, a freely accessible Natural Language Processing (NLP) tool. Thanks to its compatibility with Python, integrating BERT into your model for this specific text classification task is a seamless process.

18. Translator App

For those interested in entering the field of Natural Language Processing as a artificial intelligence projects for students, a great project to kickstart your journey is building a translator app with the assistance of a transformer. A transformer model idea of artificial intelligence projects extracts features from sentences and also determines the significance of each word within a sentence. This powerful tool consists of both encoding and decoding components, both of which are expertly trained end-to-end. 

With the help of a transformer, you have the opportunity to create your very own AI translator app. Simply load a pre-trained transformer model into your Python environment and convert your desired text into tokens to be inputted into the model. For this purpose, the GluonNLP library is highly recommended. Additionally, the same library of AI projects for final year students allows you to easily access the train and test datasets required for this exciting AI projects for final year

19. Instagram Spam Detection

Have you ever been notified of a comment on your Instagram post, only to eagerly grab your phone and find it’s yet another sneaky bot promoting bogus shoes? The comment sections of countless Instagram posts are infiltrated with these machines. Some simply annoy, while others can be outright dangerous, demanding action from you. But fear not – with the help of AI projects for final year or artificial intelligence project ideas techniques, you can create a powerful spam detection model to distinguish between spam and genuine comments.

While it may be challenging to locate a dataset specifically dedicated to Instagram spam comments, there are ways to gather this crucial information for your analysis. One such method is web scraping, through which you can access unlabelled comments from Instagram using the Python programming language. Alternatively, you could utilize a different dataset for training purposes, such as the YouTube spam collection dataset found on Kaggle. 

To classify commonly used spam words, you can implement techniques like N-Gram, which assigns weighting to certain words. These designated words can then be compared to the scraped comments to determine their level of spam. 

Additionally, utilizing a distance-based algorithm like cosine similarity can also be effective in achieving more accurate results. This kind of AI projects for students work particularly well when combined with proper pre-processing techniques tailored to the specific type of data being analyzed.

By removing stop-words, whitespaces, and punctuation from the data and ensuring proper cleaning techniques, the algorithm’s performance greatly improves. This allows for a more accurate matching of similar words. For even better results, consider utilizing a pre-trained model such as ALBERT. 

While distance or weightage matching algorithms can effectively find similar words, they may struggle to understand the full context of a sentence. To enhance context comprehension, NLP models like BERT and ALBERT should be utilized as they take into account key elements such as sentence context, coherence, and interpretability.

20. Objection Detection System

Using computer vision techniques, an object detection system has the capability to recognize various types of objects within an image. Imagine an image that includes a snapshot of someone typing on a laptop. In this scenario, the object detection system should be capable of accurately identifying and labeling both the person (human) and the laptop, as well as their respective positions within the image. 

To accomplish this task, the Kaggle Open Images Object Detection dataset is available for use. Additionally, there exists a pre-trained and open-sourced object detection model known as SSD, which was specifically trained on the COCO dataset consisting of everyday objects such as tables, chairs, and books. By further training the output layer of this model with the Kaggle Open Images dataset, one can construct their own customized object detection system as part of one of the most interesting AI projects for students.

21. Animal Species Prediction

A fascinating computer vision AI based projects for final year to consider is predicting the species of an animal using an image. An exciting dataset to work with for this is Animals-10 on Kaggle, which contains a diverse array of animals such as dogs, cats, horses, spiders, butterflies, chickens, and more. Utilizing multi-class classification techniques, you will be challenged to accurately identify the species of an animal by analyzing its picture within the dataset.

In such AI projects, utilizing a pre-trained model like VGG-16 can definitely make your life easier. This vast dataset encompasses diverse objects, from everyday items and fruits to vehicles and various animal species. Once you’ve successfully loaded the VGG-16 model into Python, you can effortlessly fine-tune it with the labeled images from the Kaggle dataset in order to accurately classify ten different types of animals.

22. Image to Pencil Sketch App

Imagine a web application that can transform any image into a stunning pencil sketch with just a click. Sounds exciting? Let’s break down the steps to make it happen: 

  • First, create a front-end application using HTML and JavaScript, which will let users upload their desired images. 
  • Next, we will dive into the back end and utilize Python, along with the powerful OpenCV library. This library has a package that specifically enables us to convert images into grayscale, invert colors, and smooth out any imperfections, giving it a realistic sketch-like appearance. 
  • Finally, it’s time to share the masterpiece with the user by displaying the final image on the screen. Get ready to impress with your sophisticated creation.

Creating AI projects for beginners may seem straightforward nowadays, thanks to the existence of libraries that can handle image conversion on our behalf. However, the true challenge lies in constructing a functional app that allows users to interact with the AI, as it demands proficiency in languages beyond Python.

Sign Language Recognition App

Learning sign language to interact with people who have hearing disabilities can be a daunting task. That is where this project of building a sign-language recognition app using Python comes in. This involves taking the following steps: 

  • Utilizing the comprehensive World-Level American Sign Language video dataset, which encompasses over 2000 classes of sign languages. 
  • Extracting frames from the dataset to train the model. 
  • Loading the Inception 3D model, pre-trained on the ImageNet dataset. 
  • Training a few dense layers on top of the I3 model using the extracted frames. This step is essential in generating corresponding text labels for the sign language gesture image frames.

After completing the model, you have the option to deploy it as part of the AI projects. This not only builds an application but also serves as a valuable tool for those with hearing disabilities, enabling them to communicate with those who do not know ASL. It bridges the gap in communication between two individuals who may not have had the chance to converse otherwise.

Identifying Violence in Videos

Videos with violent or sensitive content can have a detrimental impact on one’s mental well-being. Implementing trigger warnings or censoring this type of content can greatly benefit those who may not wish to view it. 

A solution to this issue could be utilizing the power of deep learning to work on different AI projects. By creating a model that can accurately detect violence in videos, it can automatically generate a warning for viewers to proceed with caution. This artificial intelligence projects presents an opportunity to develop such a model, which can effectively identify and flag potentially harmful content.

To train this model, a dataset containing a range of violent and non-violent videos can be utilized (links provided below). By extracting image frames from these videos and analyzing them with a Convolutional Neural Network (CNN), the model can learn to accurately identify violent content. 

Thanks to the use of transfer learning, individuals have successfully achieved exceptional accuracy rates of above 90% for this particular task. By utilizing AI topics for project models that have been previously trained on a vast number of general images, these models typically outperform ones that are trained from the ground up.

Wrapping up: Learn AI the Smart Way

In this article, we have covered 22 Artificial Intelligence project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your AI skills, you need to get your hands on these Artificial Intelligence project ideas.

As our lives (both personal and work) become deeply tied with artificial intelligence and machine learning, we have to account for its importance. To sustain and grow in your professional lives, you must familiarize yourself with artificial intelligence topics or AI topics. 

Practical knowledge will help you in the future. So, when you come across interesting topics in artificial intelligence, why don’t you bet on yourself and take up the challenge of working on a project idea? The abundance of artificial intelligence topics may be confusing. But we are here to help.

You can also check IIT Delhi’s Executive PG Programme in Machine Learning & AI 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.

Learning AI can be quite easy if you have the right guidance, mindset, and study material. We’re sure that these projects will help you in enhancing your expertise in artificial intelligence. And by looking at the variety of projects present, you must’ve figured out how powerful AI is.

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Frequently Asked Questions (FAQs)

1. What are Artificial Intelligence projects?

Artificial Intelligence (AI) initiatives are clever projects that enable machines to perform tasks that would otherwise require human intelligence. Learning, thinking, problem-solving, and perception are all goals of these intelligent creatures. Many theories, methodologies, and technologies are used in AI. Machine learning, neural networks, deep learning, cognitive computing, machine vision, and nlp are just a few of the subfields. Graphical processing unit, Iot, Advanced algorithms, and API are some of the other AI-supporting technologies.
 

2. How do I start an AI project?

Developing abilities in AI projects opens you a world of possibilities. Those interested in starting an AI project have a variety of alternatives. Enrolling in an online course is one efficient method. Choose a topic area that interests you and enroll in a course that includes real-world assignments. You need to start with the basics such as researching about the tools and software that you will need to develop the project, the approach that you need to adopt, learning about projects that are already developed and in line with the one you are working, and then putting the bits and pieces together.

3. What are the 4 types of AI?

AI can be divided into four categories. They are as follows: Reactive machines are AI systems that do not rely on prior experience to complete a task. In order to act in current situations, people with limited memory rely on their past experiences. Autonomous vehicles are an example of limited memory. Theory of mind is a form of artificial intelligence system that allows machines to make decisions. A self-aware AI system is one that is aware of its own existence. These systems should be self-aware, aware of their own condition, and able to predict the feelings of others.

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Pavan Vadapalli

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

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Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification The shift of skills in jobs Most industries in India and around the world are undergoing a digital transformation, and skills to utilise emerging technologies like mobility, cloud computing, business intelligence, artificial intelligence, machine learning, robotics and nanotechnology among others are gaining popularity. In fact, the World Economic Forum estimates that (pdf) 65% of children entering school today will ultimately end up working in jobs that don’t yet exist. For example, demand for data analysts — a relatively new occupation — increased by almost 90% by the end of 2014 within a year. Many big e-commerce players, credit firms, airlines, hospitality, BFSI and retail industries already use analytics in a major way. In India, the analytics and business intelligence industry together is sized around 10 billion and is expected to grow by 22% to 26.9 billion by 2017. Skill deprivation: Education alone won’t guarantee a job! Human cognition will be in demand in the automation age When we speak of manual work being supplanted by technology, we must keep in mind that routine jobs are most susceptible to being replaced by automation. And while non-cognitive and routine work is decreasing, knowledge-oriented work is increasing. The demand for labour adept at managing such technology is on the rise – a trend that is likely to intensify as our processes become more technologically complex and disruptive. Humans are discovering newer ways of enhancing their productivity and efficiency. Most of the pattern-driven work is slowly getting automated as technology presents new ways to speed it up. But this doesn’t mean humans will be useless. They will be the ones who will need to identify problems and ask the right questions. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Demand for newer jobs will remain History shows us that jobs have consistently been rendered obsolete with the advent of technology and machines. When the washing machine was invented, those who professionally hand-washed clothes faced large-scale unemployment and redundancy. People had to learn a more complex skill in a similar area or enter a new profession altogether. Similarly, drivers may be out of jobs if driverless cars become a norm in the future but other jobs that require manufacturing, programming and sale of such cars will have high demand. This is the way old jobs metamorphose into new ones and the economy learns to keep up. There’ll Be A Billion-Plus Job-Seekers By 2050! India ripe for tech driven roles The world is set for a technology boom with information technology jobs expected to grow by 22% through 2020 — and India is one of the leaders of the troupe. To capitalise, young job-seekers have to train themselves and take charge of technology-driven roles such as product managers, application developers, data analysts and digital marketers among others. And the rising number of startups in India, especially in the online space, provides a fertile ground. In fact, software startups in India are going to create 80,000 jobs by the following year itself. So jobs that seem to be at risk, may be like molecules – splitting further and creating more jobs – just of a different kind. Instead of worrying about unemployment, those entering the workforce need to keep one finger on the pulse of evolving technology, and invest in training themselves to acquire new skill sets. 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
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by Mayank Kumar

07 Jul'16
Keep an Eye Out for the Next Big Thing: Machine Learning

5.2K+

Keep an Eye Out for the Next Big Thing: Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords that are increasingly being used to discuss upcoming trends in Data Science and other technologies. However, are these two concepts really peas in the same pod? Artificial Intelligence is a broader concept of smart machines carrying out various tasks on their own. While Machine Learning is an application of Artificial Intelligence where machines learn from data provided to them using various types of algorithms. Therefore, Machine Learning is a method of data analysis that automates analytical model building, allowing computers to find hidden insights without being explicitly programmed to do so. Sounds like the pitch-perfect solution to all our technological woes, doesn’t it? Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Evolution of Machine Learning Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term ‘Machine Learning’ in 1959 while at IBM. During its early days, Machine Learning was born from pattern recognition with the theory that computers can learn from patterns in data without being programmed to perform specific tasks. Researchers interested in Artificial Intelligence later developed algorithms with which computers or machines could learn from data. As a result of this, whenever the machines were exposed to new data, they were able to independently adapt as well Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. It’s a science that’s not new, but one that’s gaining fresh momentum, thanks mainly to new computing technologies that have evolved over the last few decades. Many Machine Learning algorithms have been around for a long time. But, the ability to automatically apply complex mathematical calculations to large data sets is a fresh development being witnessed. Here are a few examples of Machine Learning applications you might be familiar with: Online recommendations from Amazon and Netflix. YouTube detecting and removing terror content on the platform. Knowing what customers are saying about you on Twitter The Rise of Machine Learning The emergence of the internet, as well as the massive increase in digital information being generated, stored, and made available for analysis, are seen to be the two important factors that have led to the emergence of Machine Learning. With the magnitude of quality data from the internet, economical data storage options and improved data processing capabilities, Machine Learning algorithms are seen as a vehicle propelling the development of Artificial Intelligence at a scorching pace in recent times. Neural Networks A neural network works on a system of probability by being able to make statements, decisions, or predictions based on data fed to it. Moreover, a feedback loop enables further “learning” by sensing; it also modifies the learning process based on whether its decisions are right or wrong. An artificial neural network is a computer system with node networks inspired from the neurons in the animal brain. Such networks can be taught to recognise and classify patterns through witnessing examples rather than telling the algorithm how exactly to recognise and classify patterns. Machine Learning derived applications of neural networks can read pieces of text and recognise the nature of the text – whether it is a complaint or congratulatory note. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of similar music. What’s more, they can even compose music expressing the same mood or theme. In the near future, with the help of Machine Learning and Artificial Intelligence, it should be possible for a person to communicate and interact with electronic devices and digital information thanks to another emerging field of AI called Natural Language Processing (NLP). NLP has become a source of cutting-edge innovation in the past few years, and one which is heavily reliant on Machine Learning. NLP applications attempt to understand human communication, both written as well as spoken, and communicate using various languages. In this context, Machine Learning helps machines understand the nuances in human language and respond in a way that a particular audience is likely to comprehend. So, who is actually using it? Most industries working with large amounts of data have recognised the value of Machine Learning. Large companies glean vital real-time actionable insights from stored data and are hence able to increase efficiency or gain an advantage over their competitors. Financial services Banks and other businesses use Machine Learning to identify important insights in data generated and thereby prevent frauds. These insights can identify investment opportunities or help investors know when to trade. Data mining can also identify clients with high-risk profiles or use cyber surveillance to warn customers about fraud and thereby minimise identity theft. Marketing and sales E-commerce websites use Machine Learning technology to analyse buying history based on previous purchases, to recommend items that you may like and promote other items. The retail industry is enlisting the ability of websites to capture data, analyse it, and use it to personalise a shopping experience or implement marketing campaigns. Summing up, Artificial Intelligence and, in particular, Machine Learning, certainly has a lot to offer today. With its promise of automating mundane tasks as well as offering creative insights, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. 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 Eventually, scientists hope to develop human-like Artificial Intelligence that is capable of increasing the speed of various automated functions, especially with the advent of chatbots in the internet realm. Much of the exciting progress that we have seen in recent years is due to progressive changes in Artificial Intelligence, which have been brought about by Machine Learning. This is clearly why Machine Learning is poised to become the next big thing in the data sciences sphere. So go ahead, UpGrad yourself to stay ahead of the curve.
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by Varun Dattaraj

17 Oct'17
The Difference between Data Science, Machine Learning and Big Data!

7.86K+

The Difference between Data Science, Machine Learning and Big Data!

Many professionals and ‘Data’ enthusiasts often ask, “What’s the difference between Data Science, Machine Learning and Big Data?” This is a question frequently asked nowadays. Here’s what differentiates Data Science, Machine Learning and Big Data from each other: Data Science Data Science follows an interdisciplinary approach. It lies at the intersection of Maths, Statistics, Artificial Intelligence, Software Engineering and Design Thinking. Data Science deals with data collection, cleaning, analysis, visualisation, model creation, model validation, prediction, designing experiments, hypothesis testing and much more. The aim of all these steps is just to derive insights from data. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Digitisation is progressing at an exponential rate. Internet accessibility is improving at breakneck speed. More and more people are getting absorbed into the digital ecosystem. All these activities are generating a humongous amount of data. Companies are currently sitting on a data landmine. But data, by itself, is not of much use. This is where Data Science comes into the picture. It helps in mining this data and deriving insights from it; for taking meaningful action. Various Data Science tools can help us in the process of insight generation. If you are a beginner and interested to learn more about data science, check out our data scientist courses from top universities. Frameworks exist to help derive insights from data. A framework is nothing but a supportive structure. It’s a lifecycle used to structure the development of Data Science projects. A lifecycle outlines the steps —  from start to finish — that projects usually follow. In other words, it breaks down the complex challenges into simple steps. This ensures that any significant phase, which leads to the generation of actionable insights from data, is not missed out. One such framework is the ‘Cross Industry Standard Process for Data Mining’, abbreviated as the CRISP-DM framework. The other is the ‘Team Data Science Process’ (TDSP) from Microsoft. Let’s understand this with the help of an example. A bank named ‘X’, which has been in business for the past ten years. It receives a loan application from one of its customers. Now, it wants to predict whether this customer will default in repaying the loan. How can the bank go about achieving this task? Like every other bank, X must have captured data regarding various aspects of their customers, such as demographic data, customer-related data, etc. In the past ten years, many customers would have succeeded in repaying the loan, but some customers would have defaulted. How can this bank leverage this data to improve its profitability? To put it simply, how can it avoid providing loans to a customer who is very likely to default? How can they ensure not losing out on good customers who are more likely to repay their debts? Data Science can help us resolve this challenge. Raw Data —> Data Science —-> Actionable Insights Let’s understand how various branches of Data Science will help the bank overcome its challenge. Statistics will assist in the designing of experiments, finding a correlation between variables, hypothesis testing, exploratory data analysis, etc. In this case, the loan purpose or educational qualifications of the customer could influence their loan default. After performing data cleaning and exploratory study, the data becomes ready for modeling. Statistics and artificial intelligence provide algorithms for model creation. Model creation is where machine learning comes into the picture. Machine learning is a branch of artificial intelligence that is utilised by data science to achieve its objectives. Before proceeding with the banking example, let’s understand what machine learning is. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Machine Learning “Machine learning is a form of artificial intelligence. It gives machines the ability to learn, without being explicitly programmed.” How can machines learn without being explicitly programmed, you might ask? Aren’t computers just devices made to follow instructions? Not anymore. Machine learning consists of a suite of intelligent algorithms, enabling machines to learn without being explicitly programmed for it. Machine learning helps you learn the objective function — which maps the inputs to the target variable, or independent variables to the dependent variables. In our banking example, the objective function determines the various demographics, customer and behavioural variables which influences the probability of a loan default. Independent attributes or inputs are the demographic, customer and behavioural variables of a customer. The dependent variable is either ‘to default’ or not. The objective function is an equation which maps these inputs to outputs. It’s a function which tells us which independent variables influence the dependent variable, i.e. the tendency to default. This process of deriving an objective function, which maps inputs to outputs is known as modelling. Initially, this objective function will not be able to predict precisely whether a customer will default or not. As the model encounters new instances, it learns and evolves. It improves as more and more examples become available. Ultimately, this model reaches a stage where it will be able to tell with a certain degree of precision. hings like, which customer is going to default, and whom the bank can rely on to improve its profitability. Machine learning aims to achieve ‘generalisability’. This means, the objective function — which maps the inputs to the output — should apply to the data, which hasn’t encountered it, yet. In the banking example, our model learns patterns from the data provided to it. The model determines which variables will influence the tendency to default. If a new customer applies for a loan, at this point, his/her variables are not yet seen by this model. The model should be relevant to this customer as well. It should predict reliably whether this customer will default or not. If this model is unable to do this, then it will not able to generalise the unseen data. It is an iterative process. We need to create many models to see which work, and which don’t. Data science and analysis utilise machine learning for this kind of model creation and validation. It is important to note that all the algorithms for this model creation do not come from machine learning. They can enter from various other fields. The model needs to be kept relevant at all times. If the conditions change, then the model — which we created earlier — may become irrelevant. The model needs to be checked for its predictability at different times and needs to be modified if its predictability reduces. For the banking employee to take an instant decision the moment a customer applies for a loan, the model needs to be integrated with the bank’s IT systems. The bank’s servers should host the model. As a customer applies for a loan, his variables must be captured from a website and utilised by the model running on the server. Then, this model should convey the decision — whether the credit can be granted or not — to the bank employee, instantly. This process comes under the domain of information technology, which is also utilised by data science. In the end, it is all about communicating the results from the analysis. Here, the presentation and storytelling skills are required to demonstrate the effects from the study efficiently. Design-thinking helps in visualising the results, and effectively tell the story from the analysis. Big Data The final piece of our puzzle is ‘Big Data’. How is it different from data science and machine learning? According to IBM, we create 2.5 Quintillion (2.5 × 1018) bytes of data every day! The amount of data which companies gather is so vast that it creates a large set of challenges regarding data acquisition, storage, analysis and visualisation. The problem is not entirely regarding the quantity of data that is available, but also its variety, veracity and velocity. All these challenges necessitated a new set of methods and techniques to deal with the same. Big data involves the four ‘V’s — Volume, Variety, Veracity, and Velocity — which differentiates it from conventional data. Volume: The amount of data involved here is so humongous, that it requires specialised infrastructure to acquire, store and analyse it. Distributed and parallel computing methods are employed to handle this volume of data. Variety: Data comes in various formats; structured or unstructured, etc. Structured means neatly arranged rows and columns. Unstructured means that it comes in the form of paragraphs, videos and images, etc. This kind of data also consists of a lot of information. Unstructured data requires different database systems than traditional RDBMS. Cassandra is one such database to manage unstructured data. Veracity:  The presence of huge volumes of data will not lead to actionable insights. It needs to be correct for it to be meaningful. Extreme care needs to be taken to make sure that the data captured is accurate, and that the sanctity is maintained, as it increases in volume and variety. 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 Velocity: It refers to the speed at which the data is generated. 90% of data in today’s world was created in the last two years alone. However, this velocity of information generated is bringing its own set of challenges. For some businesses, real-time analysis is crucial. Any delay will reduce the value of the data and its analysis for business. Spark is one such platform which helps analyse streaming data. As time progresses, new ‘V’s get added to the definition of big data. But — volume, variety, veracity, and velocity — are the four essential constituents which differentiate data from big data. The algorithms which deal with big data, including machine learning algorithms, are optimised to leverage a different hardware infrastructure, that is utilised to handle big data. To summarise, Executive PG Programme in Data Science is an interdisciplinary field with an aim to derive actionable insights from data. Machine learning is a branch of artificial intelligence which is utilised by data science to teach the machines the ability to learn, without being explicitly programmed. Volume, variety, veracity, and velocity are the four important constituents which differentiate big data from conventional data.
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Natural Language Generation: Top Things You Need to Know

6.11K+

Natural Language Generation: Top Things You Need to Know

From a linguistic point of view, language was created for the survival of human beings. The effective communication helped a primitive man to hunt, gather and survive in groups. This means a language is necessary to carry out all activities needed for not only survival but also a meaningful existence of human beings. As humans evolved so did their literary skills. From pictorial scripts to well developed universal ones, we have made an impressive progress. In fact, such remarkable progress that a machine developed by humans now can read data, write text and not in a machine, binary language but a real, conversational language. Natural Language Generation has made this possible. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification What is Natural Language Generation? Natural language is an offshoot of Artificial Intelligence. It is a tool to automatically analyse data, interpret it, identify the important information and narrow it down to a simple text, to make decision making in business easier, faster and of course, cheaper. It crunches numbers and drafts a narrative for you. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What are the different variations of Natural Language Generation? Basic Natural Language Generation: The basic form of NLG converts data into text through Excel-like functions. For example, a mail merge that restates numbers into a language. Templated Natural Language Generation: In this type of NGL tool, a user takes the call on designing content templates and interpreting the output. Templated systems are restricted in their capability to scan multiple data sources, perform advanced analytics. Advanced Natural Language Generation: It is the ‘smartest’ way of analysing data. It processes the data right from the beginning and separates it based on its significance for a particular audience, and then writes the narrative with relevant information in a conversational tone. For example, if a data analyst wants to know how a particular product is doing in a market, an advanced NLG tool would write a report by segregating the data of only the required product. Do we really need natural language generation? A number of devices are connected to the internet creating a huge Internet of Things. All these devices are creating data at a lightning speed leading to Big Data generation. It is almost humanly impossible to analyse, interpret and draw rational interference from this enormous data. Along with data analysis and accurate interpretation the need for the optimum use of resources, cost cutting and time management are the essentials for a modern business to survive, grow and flourish. Natural Language Generation helps up to effectively achieve all these goals in one go. Additionally, when a machine can do these routine tasks, and accurately. So, valuable human resources can indulge themselves in the activities that require innovation, creativity and problem-solving. Will Natural Language Generation kill jobs? First of all, not all kinds of narratives can be written by Natural Language Generation tools. It is only for creating a text based on data. Creative writing, engaging content is developed not only by analytical skills but with the help of major emotional involvement. The passion of an individual, their skills, their ability to cater complex terms in simpler formats can’t be replaced. Additionally, to rationalise the text created by Natural Language Generation tools, human intervention is critical. Natural Language Generation only augments the job and enriches the life of employees by freeing them from menial jobs. Alain Kaeser, founder of Yseop has rightly acknowledged that- “The next industrial revolution will be the artificial intelligence revolution and the automation of knowledge work and repetitive tasks to enhance human capacity”. Why should you get a hang of Natural Language Generation? A research commissioned by Forrester Research anticipated a 300% increase in investment in artificial intelligence in 2017 compared to 2016. The Artificial Intelligence market will grow from $8 billion in 2016 to more than $47 billion in 2020. Based on this report, Forbes magazine has come up with a list of the ‘hottest ten Artificial Intelligence technologies’ that will rule the market in the near future. Natural Language Generation is one of them and it is set to see a huge boost. Examples and Applications of Natural Language Generation Natural Language Generation techniques are put to use across various industries as per their requirements. Healthcare-Pharma, Banking services, Digital marketing… it’s everywhere! From fund reporting in finance and campaign analytics reporting in marketing to personalised client alerts for preparing dashboards in sales and customer service maintenance, it is used to generate effective results for all departments in an organisation. Let’s have a quick look at how NLG has varied applications in various departments: Marketing – Two main responsibilities of a marketing department are designing market strategy and conducting market research. Both of these activities heavily depend on data analysis, and in today’s world of big data, it is becoming increasingly complex. Natural Language Generation tools can help you scan big data, analyse it and write reports for you within a few hours. Sales – A sales analysis report indicates the trends in a company’s sales volume over a period of time. A sales analysis report throws light on the factors that affects sales, like season, competitors strategy, advertising efforts etc. Managers use sales analysis reports to recognise market opportunities and areas where they could increase volume. These reports are purely based on humongous data. Natural Language Generation programs save your time and efforts of manually scanning data, finding trends and writing reports. Once you feed the inputs, it takes care of all of these activities. Banking and finance – May it be a finance department of an organisation or an investment bank, financial reports stating the financial health of a company needs to be written and sent out to shareholders, investors, rating agencies, government agencies etc. The general financial statements like balance sheets, Statement of cash flows, Income statement etc. are loaded with numbers and a reader likes to have a quick understanding of these statements. Natural Language Generation software scans through these statements and presents this information in a simple, text format rather than complicated accounting one. Healthcare and medicine – Recently Natural Language Generation tools are being used to summarise e-medical records. Additional research in this area is opening doors to prudent medical decision-making for medical professionals. It is also being used in communicating with patients, as a part of patient awareness programs in India, as per the NCBI report. The data collected through medical research like what kind of lifestyle diseases are most dreadful or what kinds of habits are healthy can be summarized in a simple language for patients which is extremely useful for the doctors to make a case for their advice. And this is just the tip of the iceberg. The applications of NLG tools are widespread already and are ready to take off to greater heights in the future.   Techniques of natural language generation – How to get started A refined Natural Language Generation system needs to inject some aspects of planning and amalgamation of information to enable the NLG tools to generate the text which appears natural and interesting. The general stages of natural language generation, as proposed by Dale and Reiter in their book ‘Building Natural Language Generation Systems’ are: Content determination: In this stage, a data analyst must decide what kind of information to present by using their discretion with respect to relevance. For example, deciding what kind of information a share trader would want to know vs what kind of information a dealer in the commodity market would want to know. Document structuring: In this stage, a user will have to decide the sequence, format of content and the desired template. For example, to decide the order of large cap, mid cap, small cap shares while writing a narrative about equity movement in the stock market. Aggregation: No repetition is the basic rule of any report writing. To keep it simple and improve readability, merging sentences, omitting repetitive words, phrases etc, falls under this stage. For example, if NLG software is writing a report on sales and there is no substantial change in volume of sales for a few months, there are chances NLG software might write repetitive paragraphs for no substantial information. You will then have to condense it in a way it does not become long and boring. Lingual choice: Deciding what words to use exactly to describe particular concepts. For example, deciding whether to use the word ‘medium’ or ‘moderate’ while describing a change. Best software products available for natural language generation There are a variety of software products available to help you get started with Natural Language Generation. Quill, Syntheses, Arria, Amazon Polly, Yseop are popular ones. You can make a decision based on the industry you are operating in, for the department you will be deploying the tool, exact nature of report creation, etc. Let us see what kind of aid does these programs offer to the businesses. Yseop: Yseop Compose’s Natural Language Generation software enables data-driven decision making by explaining insights in a plain language. Yseop Compose is the only multilingual Natural Language Generation software and hence truly global. Amazon Polly: It is a software that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Arria: Arria NLG Platform is the one that integrates cutting-edge techniques in data analytics, artificial intelligence and computational linguistics. It analyses large and diverse data sets and automatically writes tailored, actionable reports on what’s happening within that data, with no human intervention, at vast scale and speed. Quill: It is an advanced NLG platform which comprehends user intent and performs relevant data analysis to deliver Intelligent Narratives—automated stories full of audience-relevant, insightful information. Synthesys: It is one of the popular NLG software products that scans through all data and highlights the important people, places, organizations, events and facts being discussed, resolve highlighted points and determines what’s important, connecting the dots together and figures out what the final picture means by comparing it with the opportunities, risks and anomalies users are looking for. Natural Language Generation tools automate analysis and increase the efficacy of Business Intelligence tools. Rather than generating charts and tables, NLG tools interpret the data and draft analysis in a written form that communicates precisely what’s important to know. These tools perform regular analysis of predefined data sets, eliminate the manual efforts required to draft reports and the skilled labour required to analyse and interpret the results. 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 What are the best resources to learn Natural Language Generation? Gartner, a leading research and advisory company forecasts that most companies will have to employ a Chief Data officer by 2019. With the gigantic amount of data available, it is important to decide which information can add business value, drive efficiency and improve risk management. This will be the responsibility of Data Officers. With increasing global demand for the profession, there can be no better time to learn about Natural Language Generation which is a critical part of Data Science and Artificial Intelligence. Though Natural Language generation has a huge scope, there are very few comprehensive academic programs designed to train candidates to be future ready. However, with a great vision, UpGrad offers a PG Diploma in Machine Learning and AI, in partnership with IIIT-Bangalore, which aims to build highly skilled professionals in India to cater to the increasing global demand. It gives you a chance to learn from a comprehensive collection of case-studies, hand-picked by industry experts, to give you an in-depth understanding of how Machine Learning & Artificial Intelligence impact industries like Telecom, Automobile, Finance & more. What are you waiting for? Don’t let go of this wonderful opportunity, start exploring today!
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by Maithili Pradhan

30 Jan'18
A Beginner’s Guide To Natural Language Understanding

8.29K+

A Beginner’s Guide To Natural Language Understanding

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” – Alan Turing 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 The entire gamut of artificial intelligence is based on machines being able to ‘understand’ and ‘respond’ to human beings. Which is impossible without the capability of machines to interact with humans in their natural language, like other human beings. Moreover, understanding does not involve the mere exchange of information and data but an exchange of emotions, feelings, ideas and intent. Can machines ever do that? Well, the answer is affirmative and it is not even that surprising anymore. What is this miraculous technology that smoothly facilitates the interaction between humans and machines? It is Natural Language Understanding. What is Natural Language Understanding? Natural Language Understanding is a part of Natural Language Processing. It undertakes the analysis of content, text-based metadata and generates summarized content in natural, human language. It is opposite to the process of Natural Language Generation. NLG deals with input in the form of data and generates output in the form of plain text while Natural Language Understanding tools process text or voice that is in natural language and generates appropriate responses by summarizing, editing or creating vocal responses. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Natural Language Understanding Vs Natural Language Processing Natural Language Processing is a wide term which includes both Natural Language Understanding and Natural Language Generations along with many other techniques revolving around translating and analysing natural language by machines to perform certain commands.    Examples of Natural Language Processing Natural Language Processing is everywhere and we use it in our daily lives without even realising it. Do you know how spam messages are separated from your emails? Or autocorrect and predictive typing that saves so much of our time, how does that happen? Well, it is all part of Natural Language Processing. Here are some examples of Natural Language Processing technologies used widely: Intelligent personal assistants – We are all familiar with Siri and Cortana. These mobile software products that perform tasks, offer services, with a combination of user input, location awareness, and the ability to access information from a variety of online sources are undoubtedly one of the biggest achievements of natural language processing. Machine translation – To read a description of a beautiful picture on Instagram or to read updates on Facebook, we all have used that ‘see translation’ command at least once. And google translation services helps in urgent situations or sometimes just to learn few new words. These are all examples of machine translations, where machines provide us with translations from one natural language to another. Speech recognition – Converting spoken words into data is an example of natural language processing. It is used for multiple purposes like dictating to Microsoft Word, voice biometrics, voice user interface, etc. Affective computing – It is nothing but emotional intelligence training for machines. They learn to understand your emotions, feelings, ideas to interact with you in more humane ways. Natural language generation – Natural language generation tools scan structured data, undertake analysis and generate information in text format produced in natural language. Natural language understanding – As explained above, it scans content written in natural languages and generates small, comprehensible summaries of text. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Best tools for Natural Language Understanding available today Natural Language Processing deals with human language in its most natural form and on a real-time basis, as it appears in social media content, emails, web pages, tweets, product descriptions, newspaper articles, and scientific research papers, etc, in a variety of languages. Businesses need to keep a tab on all this content, constantly. Here are a few popular natural language understanding software products which effectively aid them in this daunting task. Wolfram – Wolfram Alpha is an answer engine developed by Wolfram Alpha LLC (a subsidiary of Wolfram Research). It is an online service that provides answers to factual questions by computing the answer from externally sourced, “curated data”. Natural language toolkit – The Natural Language Toolkit, also known as NLTK, is a suite of programs used for symbolic and statistical natural language processing (NLP) for the English language. It is written in the Python programming language and was developed by Steven Bird and Edward Loper at the University of Pennsylvania. Stanford coreNLP – Stanford CoreNLP is an annotation-based NLP pipeline that offers core natural language analysis. The basic distribution provides model files for the analysis of English, but the engine is compatible with models for other languages. GATE (General Architecture for Text Engineering) – It offers a wide range of natural language processing tasks. It is a mature software used across industries for more than 15 years. Apache openNLP – The Apache OpenNLP is a toolkit based on machine learning to process natural language text. It is written in Java and is produced by Apache software foundation. It offers services like tokenizers, chucking, parsing, part of speech tagging, sentence segmentation, etc. Applications of Natural Language Understanding As we have already seen, natural language understanding is basically nothing but a smart machine reading comprehension. Now let’s have a close look at how it is used to promote the efficiency and accuracy, while saving time and efforts, of human resources, which can then be put to better use. Collecting data and data analysis – To be able to serve well, a business must know what is expected out of them. Data on customer feedback is not numeric data like sales or financial statements. It is open-ended and text heavy. For companies to identify patterns and trends throughout, this data and taking action as per identified gaps or insights, is crucial for survival and growth. More and more companies are realizing that implementing a natural language understanding solution provides strong benefits to analysing metadata like customer feedback and product reviews. Natural language understanding in such cases proves to be more effective and accurate than traditional methods like hand-coding. It helps the customer’s voice to reach you clearer and faster, which leads to effective strategizing and productive implementation. Reputation monitoring –  Customer feedback is just a tip of the iceberg as compared to the real feelings of customers about the brand. As customers, we hardly participate in customer survey feedbacks. Most of the real customer sentiments hence are trapped in unstructured data. News, blog posts, chats, and social media updates contain huge amounts of such data which is more natural and can be used to know the ‘real’ feelings of customers about the product or service. Natural language understanding software products help businesses to scan through such scattered data and draw practical inferences. Customer service – Natural Language Understanding is able to communicate with untrained individuals and can understand their intent. NLU is capable of understanding the meaning in spite of some human errors like mispronunciations or transposed letters or words. It also uses algorithms that break down human speech to structured ontology and fishes out the meaning, intent, sentiment, and the crux of human speech. One of the most important goals of NLU is to create chatbots or human interacting bots that can effectively communicate with humans without any human supervision. There are various software products like Nuance which are already involved in customer interaction. 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 Automated trading – Capital market trading automation is not a new phenomenon anymore. Multiple software products and platforms are now available that analyse market movements, the profile of industries and financial strength of a company and based on technical analysis design the trading patterns. Advanced Natural Language Understanding tools which scan through various sources like financial statements, reports, market news are the basis of automated trading systems. Market Intelligence – “What are competitors doing?” is one of the most critical information businesses need on a real-time basis. Information influences markets. Information exchange between various stakeholders designs and redesigns market dynamics all the time. Keeping a close watch on the status of an industry is essential to developing a powerful strategy, but the channels of content distribution today (RSS feeds, social media, emails) generate so much information that it’s been increasingly difficult to keep a tab on such unstructured, multi-sourced content. Financial markets have started using natural language understanding tools rigorously to keep track of information exchange in the market and help them reach it immediately. Due to such varied functions carried out by natural language understanding programs, its importance in trade, business, commerce and the industry is ever increasing. It is a smart move to learn natural language understanding programs to ensure yourself a successful career. What is the best way to learn Natural Language Understanding? The best way to prepare yourself for a brighter future in technological endeavors is to understand the algorithms of Artificial intelligence. The Post Graduate Diploma in Machine Learning and AI by UpGrad offers a chance to master concepts like Neural Networks, Natural Language Processing, Graphical Models and Reinforcement Learning. The most unique aspect of this course is the career support. And, the industry mentorship, which will help you prepare yourself for intense competition in the industry, within your actual job. So, let’s learn to use software products widely used in industry mentioned earlier like NLKT. This program aims at producing well-rounded data scientists and AI professionals with thorough knowledge of mathematics, expertise in relevant tools/languages and understanding of cutting-edge algorithms and applications. Start preparing today for a better tomorrow! Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
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by Maithili Pradhan

30 Jan'18
Neural Networks for Dummies: A Comprehensive Guide

10.94K+

Neural Networks for Dummies: A Comprehensive Guide

Our brain is an incredible pattern-recognizing machine. It processes ‘inputs’ from the outside world, categorizes them (that’s a dog; that’s a slice of pizza; ooh, that’s a bus coming towards me!), and then generates an ‘output’ (petting the dog; the yummy taste of that pizza; getting out of the way of the bus!). 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 All of this with little conscious effort, almost impulsively. It’s the very same system that senses if someone is mad at us, or involuntarily notices the stop signal as we speed past it. Psychologists call this mode of thinking ‘System 1’, and it includes innate skills — like perception and fear — that we share with other animals. (There’s also a ‘System 2’, to know more about it, check out the extremely informative Thinking, Fast and Slow by Daniel Kahneman). How is all of this related to Neural Networks, you ask? Wait, we’ll get there in a second. Look at the image above, just your regular numbers, distorted to help you explain the learning of Neural Networks better. Even looking cursorily, your mind will prompt you with the words “192”. You surely didn’t go “Ah, that seems like a straight line, I think it’s a 1”. You didn’t compute it – it happened instantly. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Fascinating, right? There is a very simple reason for this – you’ve come across the digit so many times in your life, that by trial and error, your brain automatically recognizes the digit if you present it with something even remotely close to it. Let’s cut to the chase. What exactly is a Neural Network? How does it work? By definition, a neural network is a system of hardware or softwares, patterned after the working of neurons in the human brain. Basically, it helps computers think and learn like humans. An example will make this clearer: As a child, if we ever touched a hot coffee mug and it burnt us, we made sure not to touch a hot mug ever again. But did we have any such concept of hurt in our conscience BEFORE we touched it? Not really. This adjustment of our knowledge and understanding of the world around us is based on recognizing patterns. And, like us, computers, too, learn through the same type of pattern recognition. This learning forms the whole basis of the working of neural networks. Traditional computer programs work on logic trees – If A happens, then B happens. All the potential outcomes for each of the systems can be preprogrammed. However, this eliminates the scope of flexibility. There’s no learning there. And that’s where Neural Networks come into the picture! A neural network is built without any specific logic. Essentially, it is a system that is trained to look for and adapt to, patterns within data. It is modeled exactly after how our own brain works. Each neuron (idea) is connected via synapses. Each synapse has a value that represents the probability or likelihood of the connection between two neurons to occur. Take a look at the image below: What exactly are neurons, you ask? Simply put, a neuron is just a singular concept. A mug, the colour white, tea -, the burning sensation of touching a hot mug, basically anything. All of these are possible neurons. All of them can be connected, and the strength of their connection is decided by the value of their synapse. Higher the value, better the connection. Let’s see one basic neural network connection to make you understand better: Each neuron is the node and the lines connecting them are synapses. Synapse value represents the likelihood that one neuron will be found alongside the other. So, it’s pretty clear that the diagram shown in the above image is describing a mug containing coffee, which is white in colour and is extremely hot. All mugs do not have the properties like the one in question. We can connect many other neurons to the mug. Tea, for example, is likely more common than coffee. The likelihood of two neurons being connected is determined by the strength of the synapse connecting them. Greater the number of hot mugs, the stronger the synapse. However, in a world where mugs are not used to hold hot beverages, the number of hot mugs would decrease drastically. Incidentally, this decrease would also result in lowering the strength of the synapses connecting mugs to heat. So, Becomes This small and seemingly unimportant description of a mug represents the core construction of neural networks. We touch a mug kept on a table — we find that it’s hot. It makes us think all mugs are hot. Then, we touch another mug – this time, the one kept on the shelf – it’s not hot at all. We conclude that mugs in the shelf aren’t hot. As we grow, we evolve. Our brain has been taking in data all this time. This data makes it determine an accurate probability as to whether or not the mug we’re about to touch will be hot. Neural Networks learn in the exact same way. Now, let’s talk a bit aboutthe first and the most basic model of a neural network: The Perceptron! What is a Perceptron? A perceptron is the most basic model of a neural network. It takes multiple binary inputs: x1, x2, …, and produces a single binary output. Let’s understand the above neural network better with the help of an analogy. Say you walk to work. Your decision of going to work is based on two factors majorly: the weather, and whether it is a weekday or not. The weather factor is still manageable, but working on weekends is a big no! Since we have to work with binary inputs, let’s propose the conditions as yes or no questions. Is the weather fine? 1 for yes, 0 for no. Is it a weekday? 1 yes, 0 no. Remember, we cannot explicitly tell the neural network these conditions; it’ll have to learn them for itself. How will it decide the priority of these factors while making a decision? By using something known as “weights”. Weights are just a numerical representation of the preferences. A higher weight will make the neural network consider that input at a higher priority than the others. This is represented by the w1, w2…in the flowchart above. “Okay, this is all pretty fascinating, but where do Neural Networks find work in a practical scenario?” Real-life applications of Neural Networks If you haven’t yet figured it out, then here it is, a neural network can do pretty much everything as long as you’re able to get enough data and an efficient machine to get the right parameters. Anything that even remotely requires machine learning turns to neural networks for help. Deep learning is another domain that makes extensive use of neural networks. It is one of the many machine learning algorithms that enables a computer to perform a plethora of tasks such as classification, clustering, or prediction. With the help of neural networks, we can find the solution of such problems for which a traditional-algorithmic method is expensive or does not exist. Neural networks can learn by example, hence, we do not need to program it to a  large extent. Neural networks are accurate and significantly faster than conventional speeds. Because of the reasons mentioned above and more, Deep Learning, by making use of Neural Networks, finds extensive use in the following areas: Speech recognition: Take the example of Amazon Echo Dot – magic speakers that allow you to order food, get news and weather updates, or simply buy something online just by talking it out. Handwriting recognition: Neural networks can be trained to understand the patterns in somebody’s handwriting. Have a look at Google’s Handwriting Input application – which makes use of handwriting recognition to seamlessly convert your scribbles into meaningful texts. Face recognition: From improving the security on your phone (Face ID) to the super-cool Snapchat filters – face recognition is everywhere. If you’ve ever uploaded a photo on Facebook and were asked to tag the people in your photo, you know what face recognition is! Providing artificial intelligence in games: If you’ve ever played chess against a computer, you already know how artificial intelligence powers games and game development. It’s to the extent that players use AI to improve upon their tactics and try their strategies first-hand. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau In Conclusion… Neural networks form the backbone of almost every big technology or invention you see today. It’s only fair to say that imagining deep/machine learning without neural networks is next to impossible. Depending on the way you implement a network and the kind of learning you put to use, you can achieve a lot out of a neural network, as compared to a traditional computer system. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
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by Reetesh Chandra

06 Feb'18
Neural Networks: Applications in the Real World

20.03K+

Neural Networks: Applications in the Real World

Neural Networks find extensive applications in areas where traditional computers don’t fare too well. Like, for problem statements where instead of programmed outputs, you’d like the system to learn, adapt, and change the results in sync with the data you’re throwing at it. Neural networks also find rigorous applications whenever we talk about dealing with noisy or incomplete data. And honestly, most of the data present out there is indeed noisy. 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 With their brain-like ability to learn and adapt, Neural Networks form the entire basis and have applications in Artificial Intelligence, and consequently, Machine Learning algorithms. Before we get to how Neural Networks power Artificial Intelligence, let’s first talk a bit about what exactly is Artificial Intelligence. For the longest time possible, the word “intelligence” was just associated with the human brain. But then, something happened! Scientists found a way of training computers by following the methodology our brain uses. Thus came Artificial Intelligence, which can essentially be defined as intelligence originating from machines. To put it even more simply, Machine Learning is simply providing machines with the ability to “think”, “learn”, and “adapt”. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses With so much said and done, it’s imperative to understand what exactly are the use cases of AI, and how Neural Networks help the cause. Let’s dive into the applications of Neural Networks across various domains – from Social Media and Online Shopping, to Personal Finance, and finally, to the smart assistant on your phone. You should remember that this list is in no way exhaustive, as the applications of neural networks are widespread. Basically, anything that makes the machines learn is deploying one or the other type of neural network. Social Media The ever-increasing data deluge surrounding social media gives the creators of these platforms the unique opportunity to dabble with the unlimited data they have. No wonder you get to see a new feature every fortnight. It’s only fair to say that all of this would’ve been like a distant dream without Neural Networks to save the day. FYI: Free Deep Learning Course! Neural Networks and their learning algorithms find extensive applications in the world of social media. Let’s see how: Facebook As soon as you upload any photo to Facebook, the service automatically highlights faces and prompts friends to tag. How does it instantly identify which of your friends is in the photo? The answer is simple – Artificial Intelligence. In a video highlighting Facebook’s Artificial Intelligence research, they discuss the applications of Neural Networks to power their facial recognition software. Facebook is investing heavily in this area, not only within the organization, but also through the acquisitions of facial-recognition startups like Face.com (acquired in 2012 for a rumored $60M), Masquerade (acquired in 2016 for an undisclosed sum), and Faciometrics (acquired in 2016 for an undisclosed sum). In June 2016, Facebook announced a new Artificial Intelligence initiative that uses various deep neural networks such as DeepText – an artificial intelligence engine that can understand the textual content of thousands of posts per second, with near-human accuracy. Instagram Instagram, acquired by Facebook back in 2012, uses deep learning by making use of a connection of recurrent neural networks to identify the contextual meaning of an emoji – which has been steadily replacing slangs (for instance, a laughing emoji could replace “rofl”). By algorithmically identifying the sentiments behind emojis, Instagram creates and auto-suggests emojis and emoji related hashtags. This may seem like a minor application of AI, but being able to interpret and analyze this emoji-to-text translation at a larger scale sets the basis for further analysis on how people use Instagram. Pinterest Pinterest uses computer vision – another application of neural networks, where we teach computers to “see” like a human, in order to automatically identify objects in images (or “pins”, as they call it) and then recommend visually similar pins. Other applications of neural networks at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing. Online Shopping Do you find yourself in situations where you’re set to buy something, but you end up buying a lot more than planned, thanks to some super-awesome recommendations? Yeah, blame neural networks for that. By making use of neural network and its learnings, the e-commerce giants are creating Artificial Intelligence systems that know you better than yourself. Let’s see how: Search Your Amazon searches (“earphones”, “pizza stone”, “laptop charger”, etc) return a list of the most relevant products related to your search, without wasting much time. In a description of its product search technology, Amazon states that its algorithms learn automatically to combine multiple relevant features. It uses past patterns and adapts to what is important for the customer in question. And what makes the algorithms “learn”? You guessed it right – Neural Networks! Recommendations Amazon shows you recommendations using its “customers who viewed this item also viewed”,  “customers who bought this item also bought”, and also via curated recommendations on your homepage, on the bottom of the item pages, and through emails. Amazon makes use of Artificial Neural Networks to train its algorithms to learn the pattern and behaviour of its users. This, in turn, helps Amazon provide even better and customized recommendations. Banking/Personal Finance Cheque Deposits Through Mobile Most large banks are eliminating the need for customers to physically deliver a cheque to the bank by offering the ability to deposit cheques through a smartphone application. The technologies that power these applications use Neural Networks to decipher and convert handwriting on checks into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition. Fraud Prevention How can a financial institution determine a fraudulent transaction? Most of the times, the daily transaction volume is too much to be reviewed manually. To help with this, Artificial Intelligence is used to create systems that learn through training what types of transactions are fraudulent (speak learning, speak Neural Networks!). FICO – the company that creates credit ratings that are used to determine creditworthiness, makes use of neural networks to power their Artificial Intelligence to predict fraudulent transactions. Factors that affect the artificial neural network’s final output include the frequency and size of the transaction and the kind of retailer involved. Powering Your Mobile Phones Voice-to-Text One of the more common features on smartphones today is voice-to-text conversion. Simply pressing a button or saying a particular phrase (“Ok Google”, for example), lets you start speaking to your phone and your phone converts the audio into text. Google makes use of artificial neural networks in recurrent connection to power voice search. Microsoft also claims to have developed a speech-recognition system – using Neural Networks, that can transcribe conversations slightly more accurately than humans. Smart Personal Assistants With the voice-to-text technology becoming accurate enough to rely on for basic conversations, it is turning into the control interface for a new generation of personal assistants. Initially, there were simpler phone assistants – Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar. Amazon expanded upon this model with the announcement of complementary hardware and software components – Alexa, and Echo (later, Dot). 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 To Wrap Up… We’ve only scratched the surface when it comes to the applications of neural networks in day-to-day life. Specific industries and domains have specific interactions with Artificial Intelligence by making use of neural networks which is far beyond what’s talked about in this article. For example, chess players regularly use chess engines to analyze their games, improve themselves, and practice new tactics – and it goes without saying that the chess engine in question deploys Neural Networks to accomplish the learning. Learn ML courses Online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Do you have any other interesting real-life use case of Neural Networks that we might have missed? Drop it in the comments below!
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by Reetesh Chandra

06 Feb'18
Go and the Challenge to Artificial General Intelligence

5.78K+

Go and the Challenge to Artificial General Intelligence

This article aims to explore the connection between the game ‘Go’ and artificial intelligence. The objective is to answer the questions – What makes the game of Go, special? Why was mastering the game of Go difficult for a computer? Why was a computer program able to beat a chess grandmaster in 1997? Why did it take close to two decades to crack Go? 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 “Gentlemen should not waste their time on trivial games – they should study Go” – Confucius In fact, artificial intelligence pundits thought computers would only be able to beat a world Go champion by 2027. Thanks to DeepMind, an artificial intelligence company under the umbrella of Google, this formidable task was achieved a decade earlier. This article will talk about the technologies used by DeepMind to beat the world Go champion. Finally, this post discusses how this technology can be used to resolve some complex, real-world problems. Go – What is it? Go is a 3000-year-old Chinese strategy board game, which has retained its popularity through the ages. Played by tens of millions of people worldwide, Go is a two-player board game with simple rules and intuitive strategy. Different board sizes are in use for playing this game; professionals use a 19×19 board. The game starts with an empty board. Each player then takes turns to place the black and white stones (black goes first) on the board, at the intersection of the lines (unlike chess, where you place pieces in the squares). A player can capture the stones of the opponent by surrounding it from all sides. For each captured stone, some points are awarded to the player. The objective of the game is to occupy maximum territory on the board along with capturing your opponents’ stones. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Go is about creation, unlike Chess, which is about destruction. Go requires freedom, creativity, intuition, balance, strategy and intellectual depth to master the game. Playing Go involves both sides of the brain. In fact, the brain scans of Go players have revealed that Go helps in brain development by improving connections between both the brain hemispheres. Go and the Challenge to Artificial Intelligence (AI) Computers were able to master Tic-Tac-Toe in 1952. Deep Blue was able to beat Chess grandmaster Garry Kasparov in 1997. The computer program was able to win against the world champion in Jeopardy (a popular American game) in 2001. DeepMind’s AlphaGo was able to defeat a world Go champion in 2016. Why is it considered challenging for a computer program to master the game of Go? Chess is played on an 8×8 board whereas Go uses a 19×19 size board. In the opening of a chess game, a player will have 20 possible moves. In a Go opening, a player can have 361 possible moves.The number of possible Go board positions is equal to 10 to the power 170; more than the number of atoms in our universe! The potential number of board positions makes Go googol times (10 to the power 100) more complex than chess. In chess, for each step, a player is faced with a choice of 35 moves. On average, a Go player will have 250 possible moves at each step. In Chess, at any given position, it is relatively easy for a computer to do brute force search and choose the best possible move which maximises the chances of winning. A brute force search is not possible in the case of Go, as the potential number of legal moves allowed for each step is humongous. For a computer to master chess, it becomes easier as the game progresses because the pieces are removed from the board. In Go, it becomes more difficult for the computer program as stones are added to the board as the game progresses. Typically, a Go game will last 3 times longer than a game of chess. Due to all these reasons, a top computer Go program was only able to catch up with the Go world champion in 2016, after a huge explosion of new machine learning techniques. Scientists working at DeepMind were able to come up with a computer program called AlphaGo which defeated world champion Lee Seedol. Achieving the task was not easy. The researchers at DeepMind came up with many novel innovations in the process of creating AlphaGo. “The rules of Go are so elegant, organic, and rigorously logical that if intelligent life forms exist elsewhere in the universe, they almost certainly play Go.” – Edward Laskar How AlphaGo Works AlphaGo is a general purpose algorithm, which means it can be put to use for solving other tasks as well. For example, Deep Blue from IBM is specifically designed for playing chess. Rules of chess together with the accumulated knowledge from centuries of playing the game are programmed into the brain of the program. Deep Blue can’t be used even for playing trivial games like Tic-Tac-Toe. It can do only one specific thing, which it is very good at, i.e. playing chess. AlphaGo can learn to play other games as well apart from Go. These general purpose algorithms constitute a novel field of research, called Artificial General Intelligence. AlphaGo uses state-of-the-art methods – Deep Neural Networks (DNN), Reinforcement Learning (RL), Monte Carlo Tree Search (MCTS), Deep Q Networks (DQN) (a novel technique introduced and popularised by DeepMind which combines neural networks with reinforcement learning), to name a few. It then combines all these methods innovatively to achieve superhuman level mastery in the game of Go. Let’s first look at each individual piece of this puzzle before going into how these pieces are tied together to achieve the task at hand. Deep Neural Networks DNNs are a technique to perform machine learning, loosely inspired by the functioning of the human brain. A DNN’s architecture consists of layers of neurons. DNN can recognise patterns in data without being explicitly programmed for it. It maps the inputs to outputs without anyone specifically programming it for the same. As an example, let us assume that we have fed the network with a lot of cat and dog photos. At the same time, we are also training the system by telling it (in the form of labels) if a particular image is of a cat or a dog (this is called supervised learning). A DNN will learn to recognise the pattern from the photos to successfully differentiate between a cat and a dog. The main objective of the training is that when a DNN sees a new picture of either a dog or a cat, it should be able to correctly classify it, i.e. predict if it is a cat or a dog. Let us understand the architecture of a simple DNN. The number of neurons in the input layer corresponds to the size of the input. Let us assume our cat and dog photos are a 28×28 image. Each row and column will consist of 28 pixels each, which makes it a total of 784 pixels for each picture. In such a case the input layer will comprise of 784 neurons, one for each pixel. The number of neurons in the output layer will depend on the number of classes into which the output needs to be classified. In this case, the output layer will consist of two neurons – one corresponding to ‘cat’, the other to ‘dog’. There will be many neuron layers in between the input and output layers (which is the origin of using the term ‘Deep’ in ‘Deep Neural Network’). These are called “hidden layers”. The number of hidden layers and the number of neurons in each layer is not fixed. In fact, changing these values is exactly what leads to optimisation of performance. These values are called hyper-parameters, and they need to be tuned according to the problem at hand. The experiments surrounding neural networks largely involve finding out the optimal number of hyperparameters. The training phase of DNNs will consist of a forward pass and a backward pass. First, all the connections between the neurons are initialised with random weights. During the forward pass, the network is fed with a single image. The inputs (pixel data from the image) are combined with the parameters of the network (weights, biases and activation functions) and feed-forwarded through hidden layers, all the way to the output, which returns a probability of a photo belonging to each of the classes. Then, this probability is compared with the actual class label, and an “error” is calculated. At this point, the backward pass is performed – this error information is passed back through the network through a technique called “back-propagation”. During initial phases of training, this error will be high, and a good training mechanism will gradually reduce this error. The DNNs are trained in this way with a forward and backward pass until the weights stop changing (this is known as convergence). Then the DNNs will be able to predict and classify the images with a high degree of accuracy, i.e. whether the picture has a cat or a dog. Research has given us many different Deep Neural Network Architectures. For Computer Vision problems (i.e. problems involving images), Convolution Neural Networks (CNNs) have traditionally given good results. For issues which involve a sequence – speech recognition or language translation – Recurrent Neural Networks (RNN) provide excellent results. In the case of AlphaGo, the process was as follows: first, the Convolution Neural Network (CNN) was trained on millions of images of board positions. Next, the network was informed about the subsequent move played by the human experts in each case during the training phase of the network. In the same manner as earlier mentioned, the actual value was compared with the output and some sort of “error” metric was found. At the end of the training, the DNN will output the next moves along with probabilities which are likely to be played by an expert human player. This kind of network can only come up with a step which is played by a human expert player. DeepMind was able to achieve an accuracy of 60% in predicting the move that the human would make. However, to beat a human expert at Go, this is not sufficient. The output from the DNN is further processed by Deep Reinforcement Network, an approach conceived by DeepMind, which combines deep neural networks and reinforcement learning. Deep Reinforcement Learning Reinforcement learning (RL) is not a new concept. Nobel prize laureate Ivan Pavlov experimented on classical conditioning on dogs and discovered the principles of reinforcement learning in 1902. RL is also one of the methods with which humans learn new skills. Ever wondered how the Dolphins in shows are trained to jump to such great heights out of the water? It is with the help of RL. First, the rope which is used for preparing the dolphins is submerged in the pool. Whenever the dolphin crosses the cable from the top, it is rewarded with food. When it does not cross the rope the reward is withdrawn. Slowly the dolphin will learn that it is paid whenever it passes the cord from above. The height of the rope is increased gradually to train the dolphin. Agents in reinforcement learning are also trained using the same principle. The agent will take action and interact with the environment. The action taken by the agent causes the environment to change. Further, the agent received feedback about the environment. The agent is either rewarded or not, depending on its action and the objective at hand. The important point is, this objective at hand is not explicitly stated for the agent. Given sufficient time, the agent will learn how to maximise future rewards. Combining this with DNNs, DeepMind invented Deep Reinforcement Learning (DRL) or Deep Q Networks (DQN) where Q stands for maximum future rewards obtained. DQNs were first applied to Atari games. DQN learnt how to play different types of Atari games just out of the box. The breakthrough was that no explicit programming was required for representing different kinds of Atari games. A single program was smart enough to learn about all the different environments of the game, and through self-play, was able to master many of them. In 2014, DQN outperformed previous machine learning methods in 43 of the 49 games (now it has been tested on more than 70 games). In fact, in more than half the games, it performed at more than 75% of the level of a professional human player. In certain games, DQN even came up with surprisingly far-sighted strategies that allowed it to achieve the maximum attainable score—for example, in Breakout, it learned to first dig a tunnel at one end of the brick wall, so the ball would bounce around the back and knock out bricks from behind. Policy and Value Networks There are two main types of networks inside AlphaGo: One of the objectives of AlphaGo’s DQNs is to go beyond the human expert play and mimic new innovative moves, by playing against itself millions of times and thereby incrementally improving the weights. This DQN had an 80% win rate against common DNNs. DeepMind decided to combine these two neural networks (DNN and DQN) to form the first type of network – a ‘Policy Network’. Briefly, the job of a policy network is to reduce the breadth of the search for the next move and to come up with a few good moves which are worth further exploration. Once the policy network is frozen, it plays against itself millions of times. These games generate a new Go dataset, consisting of the various board positions and the outcomes of the games. This dataset is used to create an evaluation function. The second type of function – the ‘Value Network’ is used to predict the outcome of the game. It learns to take various board positions as inputs and predict the outcome of the game and the measure of it. Combining the Policy and Value Networks After all this training, DeepMind finally ended up with two neural networks – Policy and Value Networks. The policy network takes the board position as an input and outputs the probability distribution as the likelihood of each of the moves in that position. The value network again takes the position of the board as input and outputs a single real number between 0 and 1. If the output of the network is zero, it means that white is completely winning and 1 indicates a complete win for the player with black stones. The Policy network evaluates current positions, and the value network evaluates future moves. The division of tasks into these two networks by DeepMind was one of the major reasons behind the success of AlphaGo. Combining Policy and Value networks with Monte Carlo Tree Search (MCTS) and Rollouts The neural networks on their own will not be enough. To win the game of Go, some more strategising is required. This plan is achieved with the help of MCTS. Monte Carlo Tree Search also helps in stitching the two neural networks together in an innovative way. Neural networks assist in an efficient search for the next best move. Let’s try constructing an example which will help you visualise all of this much better. Imagine that the game is in a new position, one which has not been encountered before. In such a situation, a policy network is called upon to evaluate the current situation and possible future paths; as well as the desirability of the paths and the value of each move by the Value networks, supported by Monte Carlo rollouts. Policy network finds all the possible “good” moves and value networks evaluate each of their outcomes. In Monte Carlo rollouts, a few thousand random games are played from the positions recognised by the policy network. Experiments were done to determine the relative importance of value networks against Monte Carlo rollouts. As a result of this experimentation, DeepMind assigned 80% weightage to the Value networks and 20% weightage to the Monte Carlo rollout evaluation function. The policy network reduces the width of the search from 200-odd possible moves to the 4 or 5 best moves. The policy network expands the tree from these 4 or 5 steps which need consideration. The value network helps in cutting down the depth of the tree search by instantly returning the outcome of the game from that position. Finally, the move with the highest Q value is selected, i.e. the step with maximum benefit. “The game is played primarily through intuition and feel, and because of its beauty, subtlety and intellectual depth it has captured the human imagination for centuries.” – Demis Hassabis Application of AlphaGo to real-world problems The vision of DeepMind, from their website, is very telling – “Solve intelligence. Use this knowledge to make the world a better place”. The end goal of this algorithm is to make it general-purpose so that it can be used to solve complex real-world problems. DeepMind’s AlphaGo is a significant step forward in the quest for AGI. DeepMind has used its technology successfully to solve real-world problems – let’s look at some examples: Reduction in energy consumption DeepMind’s AI was successfully utilised to reduce Google’s data centre cooling cost by 40%. In any large-scale energy consuming environment this improvement is a phenomenal step forward. One of the primary sources of energy consumption for a data centre is cooling. A lot of heat generated from running the servers needs to be removed for keeping it operational. This is accomplished by large-scale industrial equipment like pumps, chillers and cooling towers. As the environment of the data centre is very dynamic, it is challenging to operate at optimal energy efficiency. DeepMind’s AI was used to tackle this problem. First, they proceeded using historical data, which was collected by thousands of sensors within the data centre. Using this data, they trained an ensemble of DNNs on average future Power Usage Effectiveness (PUE). As this is a general-purpose algorithm, it is planned that it will be applied to other challenges as well, in the data centre environment. The possible applications of this technology include getting more energy from the same unit of input, reducing semiconductor manufacturing energy and water usage, etc. DeepMind announced in its blog post that this knowledge would be shared in a future publication so that other data centres, industrial operators and ultimately the environment can greatly benefit from this significant step. 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 Radiotherapy planning for head and neck cancers DeepMind has collaborated with the radiotherapy department at University College London Hospital’s NHS Foundation Trust, a world leader in cancer treatment. One in 75 men and one in 150 women are diagnosed with oral cancer in their lifetime. Due to the sensitive nature of the structures and organs in the head and neck area, radiologists need to take extreme care while treating them. Before radiotherapy is administered, a detailed map needs to be prepared with the areas to be treated and the areas to be avoided. This is known as segmentation. This segmented map is fed into the radiography machine, which will then target cancer cells without harming healthy cells. In the case of cancer of the head or neck region, this is a painstaking job for the radiologists as it involves very sensitive organs. It takes around four hours for the radiologists to create a segmented map for this area. DeepMind, through its algorithms, is aiming to reduce the time required for generating the segmented maps, from four to one hour. This will significantly free up the radiologist’s time. More importantly, this segmentation algorithm can be utilised for other parts of the body. To summarise, AlphaGo successfully beat the 18-time world Go champion, Lee Seedol, four times in a best-of-five tournament in 2016. In 2017, it even beat a team of the world’s best players. It uses a combination of DNN and DQN as a policy network for coming up with the next best move, and one DNN as a value network to evaluate the outcome of the game. Monte Carlo tree search is used along with both the policy and value networks to reduce the width and depth of the search – they are used to improve the evaluation function. The ultimate aim of this algorithm is not to solve board games but to invent an Artificial General Intelligence algorithm. AlphaGo is undoubtedly a big step ahead in that direction. Of course, there have been other effects. As the news of AlphaGo Vs Lee Seedol became viral, the demand for Go boards jumped tenfold. Many stores reported instances of Go boards going out of stock, and it became challenging to purchase a Go board. Fortunately, I just found one and ordered it for myself and my kid. Are you planning to buy the board and learn Go? Learn ML courses from the World’s top Universities. 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Sentiment Analysis: What is it and Why Does it Matter?

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Sentiment Analysis: What is it and Why Does it Matter?

Sentiment Analysis, also known as Opinion Mining, refers to the techniques and processes that help organisations retrieve information about how their customer-base is reacting to a particular product or service. 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 In essence, Sentiment Analysis is the analysis of the feelings (i.e. emotions, attitudes, opinions, thoughts, etc.) behind the words by making use of Natural Language Processing (NLP) tools. If you’re not aware of what NLP tools do – it’s pretty much all in the name. Natural Language Processing essentially aims to understand and create a natural language by using essential tools and techniques. Sentiment Analysis also uses Natural Language Processing and Machine Learning to help organisations look far beyond just the number of likes/shares/comments they get on an ad campaign, blog post, released product, or anything of that nature. In this article, we’ll be talking about Sentiment Analysis in great depth. From talking about the methods and tools of Sentiment Analysis to discussing why is it so extensively used – we’ve got it all covered! In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 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. Sentiment Analysis: The Math Behind It Simply reading a post will let you identify whether the author had a positive stance or a negative stance on the topic – but that’s if you’re well versed in the language. However, a computer has no concept of naturally spoken language – so, we need to break down this problem into mathematics (the language of a computer). It cannot simply deduce whether something contains joy, frustration, anger, or otherwise – without any context of what those words mean. Sentiment Analysis solves this problem by using Natural Language Processing. Basically, it recognizes the necessary keywords and phrases within a document, which eventually help the algorithm to classify the emotional state of the document. Data Scientists and programmers write applications which feeds the documents into the algorithm and stores the results in a way which is useful for clients to use and understand. Keyword spotting is one of the simplest technique and leveraged widely by Sentiment Analysis algorithms. The fed Input document is thoroughly scanned for the obvious positive and negative words like “sad”, “happy”, “disappoint”, “great”, “satisfied”, and such. There are a number of Sentiment Analysis algorithms, and each has different libraries of words and phrases which they score as positive, negative, and neutral. These libraries are often called the “bag of words” by many algorithms. Although this technique looks perfect on the surface, it has some definite shortcomings. Consider the text, “The service was horrible, but the ambiance was awesome!” Now, this sentiment is more complex than a basic algorithm can take into account – it contains both positive and negative emotions. For such cases, more advanced algorithms were devised which break the sentence on encountering the word “but” (or any contrastive conjunction). So, the result becomes “The service was horrible” AND “But the ambiance was awesome.” This sentence will now generate two or more scores (depending on the number of emotions present in the statement). These individual scores are consolidated to find out the overall score of a piece. In practice, this technique is known as Binary Sentiment Analysis. No Machine Learning algorithm can achieve a perfect accuracy of 100%, and this is no different. Due to the complexity of our natural language, most of the sentiment analysis algorithms are only 80% accurate, at best. Sentiment Analysis: Algorithms and Tools The above graphic will give you a fair idea of the classification of Sentiment Analysis algorithms. Essentially, there are two types of Machine Learning algorithms: ML-based You’re aware of the basic workings of any Machine Learning algorithms. The same route by followed in ML-based sentiment analysis algorithms as well. These algorithms require you to create a model by training the classifier with a set of example. This ideally means that you must gather a dataset with relevant examples for positive, neutral, and negative classes, extract these features from the examples and then train your algorithm based on these examples. These algorithms are essentially used for computing the polarity of a document, Lexicon-based As the name suggests, these techniques use dictionaries of words. Each word is annotated with its emotional polarity and sentiment strength. This dictionary is then matched with the document to calculate its overall polarity score of the document. These techniques usually give high precision but low recall. There is no “best” choice out of the two, your choice of method should depend solely on the problem at hand. Lexical algorithms can achieve near-perfect results, but, they require using a lexicon – something that’s not always available in all the languages. On the other hand, ML-based algorithms also deliver good results, but, they require extensive training on labeled data. The Difference between Data Science, Machine Learning and Big Data! Most Used Sentiment Analysis Tools There are many Sentiment Analysis and tracking tools available for you to use. We’ll look at five such tools that find extensive use the industry today: PeopleBrowsr PeopleBrowsr helps you find all the mentions of your industry, brand, and competitors and analyse the sentiments. It allows you to compare the number of mentions your brand had before, during, and after any ad campaigns. Meltwater Meltwater is a social media listening tool that does everything from tracking impact and sentiment analysis in real-time to understanding the competitor’s footprints. Organisations like Sodexo, TataCliq, HCL, NIIT, and many others use Meltwater to improve their online presence and impact. Google Analytics  Google Analytics helps organisations discover which channels are influencing their subscribers and customers. It helps them create reports and annotation that keeps records of all the marketing campaigns and online behaviors. HootSuite The free version of HootSuite allows the organisations to manage and measure their presence on social networks. $5.99/month will make you a premium customer that’ll entitle you to use advanced analytics features. Social Mention Socialmention is a very useful tool that allows brands to track mentions for specific keywords in blogs, microblogs, videos, bookmarks, events, comments, news, hashtags, and even audios. It also indicates if mentions are positive, negative, or neutral. How Big Data and Machine Learning are Uniting Against Cancer Sentiment Analysis: Why should it be used? With everything shifting online, Brands have started giving utmost importance to Sentiment Analysis. Honestly, it’s their only gateway to thoroughly understanding their customer-base, including their expectations from the brand. Social Media listening can help organisations from any domain understand the grievances and concerns of their customers – which eventually helps the organisations scale up their services. Sentiment Analysis helps brands tackle the exact problems or concerns of their customers. According to some researchers, Sentiment Analysis of Twitter data can help in the prediction of stock market movements. Researchs show that news articles and social media can hugely influence the stock market. News with overall positive sentiment has been observed to relate to a large increase in price albeit for a short period of time. On the other hand, negative news is seen to be linked to a decrease in price – but with more prolonged effects. Ideally, sentiment analysis can be put to use by any brand looking to: Target specific individuals to improve their services. Track customer sentiment and emotions over time. Determine which customer segment feels more strongly about your brand. Track the changes in user behavior corresponding to the changes in your product. Find out your key promoters and detractors. Clearly, sentiment analysis gives an organisation the much-needed insights on their customers. Organisations can now adjust their marketing strategies depending on how the customers are responding to it. Sentiment Analysis also helps organisations measure the ROI of their marketing campaigns and improve their customer service. Since sentiment analysis gives the organisations a sneak peek into their customer’s emotions, they can be aware of any crisis that’s to come well in time – and manage it accordingly. 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 In Conclusion… More or less every major brand these days relies heavily on social media listening to improve the overall customer experience. If you’re one of the interested souls and want to explore this topic in further depth, we recommend you go through the various kinds of algorithms (the ones we displayed in a graphic earlier) and implementations of Sentiment Analysis in more detail. Also, If you’re interested to learn more about Machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning 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.
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by Amandeep Rathee

21 Feb'18