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45+ Best Machine Learning Project Ideas For Beginners [2024]

Updated on 22 May, 2024

334.86K+ views
42 min read

Summary:

In this Article, you will learn

  1. Stock Prices Predictor
  2. Sports Predictor
  3. Develop A Sentiment Analyzer
  4. Enhance Healthcare
  5. Prepare ML Algorithms – From Scratch!
  6. Develop A Neural Network That Can Read Handwriting
  7. Movie Ticket Pricing System
  8. Iris Flowers Classification ML Project
  9. BigMart Sales Prediction ML Project
  10. Recommendation Engines with MovieLens Dataset
  11. Predicting Wine Quality using Wine Quality Dataset
  12. MNIST Handwritten Digit Classification
  13. Human Activity Recognition using Smartphone Dataset
  14. Object Detection with Deep Learning
  15. Fake News Detection…. and so on..

Read the full blog to know all the 45+ ML Projects in detail.

Machine Learning Project Ideas

As Artificial Intelligence (AI) continues to progress rapidly in 2022, achieving mastery over Machine Learning (ML) is becoming increasingly important for all the players in this field. This is because both AI and ML complement each other. So, if you are a beginner, the best thing you can do is work on some Machine Learning projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Machine Learning projects which beginners can work on to put their Machine Learning knowledge to test. In this article, you will find 15 top machine learning project ideas for beginners to get hands-on experience.

But first, let’s address the more pertinent question that must be lurking in your mind: why to build Machine Learning projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different Machine Learning projects, the more knowledge you gain.

While textbooks and study materials will give you all the knowledge you need to know about Machine Learning, you can never really master ML unless you invest your time in real-life practical experiments – projects on Machine Learning. As you start working on machine learning project ideas, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. In this tutorial, you will find 15 interesting machine learning project ideas for beginners to get hands-on experience on machine learning. 

These courses will guide you to create the best ML projects.

Learn Machine Learning Online Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

What are the uses of machine learning?

Machine learning has various uses across various industries and domains due to its ability to analyze and learn from data to make predictions, identify patterns, and automate tasks. Here are some common uses of machine learning:

  • Predictive Analytics

Predictive analytics is a cornerstone of machine learning applications. Machine learning models can predict future trends and outcomes by analyzing historical data. This is invaluable for industries such as finance, where predicting stock prices, currency exchange rates, and market trends can provide a competitive edge. Retailers also use predictive analytics to forecast demand, optimize inventory, and enhance supply chain management.

  • Image and Video Recognition

Machine learning algorithms can be trained to recognize objects, people, and patterns in images and videos. Applications include facial recognition, object detection, medical image analysis, and autonomous vehicles.

  • Natural Language Processing (NLP)

NLP is a subset of machine learning that deals with human language. It’s the foundation of voice assistants like Siri and language translation services like Google Translate. Sentiment analysis, another NLP application, helps businesses understand the public sentiment around their products or services through social media and reviews.

  • Recommendation Systems

These systems use machine learning to suggest products, services, or content to users based on their past behavior and preferences. Examples include Netflix’s movie recommendations and Amazon’s product recommendations.

  • Fraud Detection

Machine learning can detect fraudulent activities by identifying unusual patterns in data. This is used in financial institutions to detect credit card fraud, insurance fraud, and other types of scams.

  • Healthcare Applications

Machine learning has revolutionized healthcare by assisting in early disease detection, personalized treatment, and drug discovery. Models trained on medical data can identify patterns that may not be apparent to human physicians. Medical imaging analysis using machine learning aids in diagnosing conditions from X-rays, MRIs, and CT scans. Additionally, predictive models can anticipate disease outbreaks, enhancing public health responses.

  • Autonomous Vehicles

Machine learning algorithms enable self-driving cars to perceive their environment, make decisions, and navigate safely. They process data from sensors like cameras, lidar, and radar to drive autonomously.

  • Customer Segmentation

Businesses use machine learning to segment customers into groups based on their behavior, preferences, and demographics. This helps in targeted marketing and improving customer experiences.

  • Financial Analysis

Machine learning can be used to analyze large financial datasets, detect patterns, and make investment decisions. High-frequency trading, credit scoring, and risk assessment are some examples.

  • Industrial Automation

Machine learning helps optimize manufacturing processes, predict equipment failures, and manage supply chains more efficiently. It can also enhance quality control and reduce downtime.

  • Energy Management

Machine learning is used to optimize energy consumption in buildings, predict demand, and improve energy efficiency in various industries.

  • Agriculture

Machine learning aids precision agriculture by analyzing data from drones, sensors, and satellites. This helps farmers make informed decisions about irrigation, fertilization, and pest control, leading to higher crop yields and reduced resource waste.

  • Gaming and Entertainment

Machine learning is employed for character animation, game strategy optimization, and generating realistic graphics.

  • Social Media Analysis

Machine learning algorithms can analyze social media data to extract insights, sentiment analysis, and trends for businesses and researchers.

  • Environmental Monitoring

Machine learning models can process data from sensors and satellites to monitor environmental changes, weather patterns, and natural disasters.

  • Enhanced Customer Experience

Businesses leverage machine learning to understand customer preferences and behaviors, leading to better-targeted marketing and improved customer experiences. Recommendation systems, commonly seen on platforms like Netflix and Amazon, suggest products and content based on user history. Chatbots powered by machine learning offer instant customer support, enhancing engagement and satisfaction.

So, here are a few Machine Learning Projects which beginners can work on:

Here are some cool Machine Learning project ideas for beginners

Watch our video on machine learning project ideas and topics…

This list of machine learning project ideas for students is suited for beginners, and those just starting out with Machine Learning or Data Science in general. These machine learning project ideas will get you going with all the practicalities you need to succeed in your career as a Machine Learning professional. 

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

1. Stock Prices Predictor

One of the best ideas to start experimenting you hands-on Machine Learning projects for students is working on Stock Prices Predictor. Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks. And with so much data available on the stock market, it is a hotbed of opportunities for data scientists with an inclination for finance.

This is one of the best and easiest Machine Learning project ideas for not only beginners but also for final year students. However, before you start off, you must have a fair share of knowledge in the following areas:

  • Predictive Analysis: Leveraging various AI techniques for different data processes such as data mining, data exploration, etc. to ‘predict’ the behaviour of possible outcomes.
  • Regression Analysis: Regressive analysis is a kind of predictive technique based on the interaction between a dependent (target) and independent variable/s (predictor).
  • Action Analysis: In this method, all the actions carried out by the two techniques mentioned above are analyzed after which the outcome is fed into the machine learning memory.
  • Statistical Modeling: It involves building a mathematical description of a real-world process and elaborating the uncertainties, if any, within that process.  

What is Machine Learning and Why it matters

Building a Stock Prices Predictor is one of the best hands-on Machine Learning project for students, involving the collection and preprocessing of historical stock price data. Students engage in feature engineering, exploring data trends, and selecting regression algorithms for training. 

The project emphasizes model evaluation using metrics like MAE and MSE, fostering a practical understanding of machine learning concepts. Through iterative refinement, students optimize the model’s performance and gain valuable insights into data manipulation, feature engineering, and project management. The project’s real-world relevance lies in its application to financial data, making it a comprehensive learning opportunity that integrates finance and machine learning.

Source Code: Stock Prices Predictor

2. Sports Predictor

In Michael Lewis’ Moneyball, the Oakland Athletics team transformed the face of baseball by incorporating analytical player scouting technique in their gameplan. And just like them, you too can revolutionize sports in the real world! This is an excellent machine learning projects for beginners.

Since there is no dearth of data in the sports world, you can utilize this data to build fun and creative machine learning projects such as using college sports stats to predict which player would have the best career in which particular sports (talent scouting). You could also opt for enhancing team management by analyzing the strengths and weaknesses of the players in a team and classifying them accordingly.

6 Times Artificial Intelligence Startled The World

With the amount of sports stats and data available, this is an excellent arena to hone your data exploration and visualization skills. For anyone with a flair in Python, Scikit-Learn will be the ideal choice as it includes an array of useful tools for regression analysis, classifications, data ingestion, and so on. Mentioning Machine Learning projects for the final year can help your resume look much more interesting than others.

Source Code: SportsPredictor

3. Develop A Sentiment Analyzer

This is one of the interesting machine learning project ideas. Although most of us use social media platforms to convey our personal feelings and opinions for the world to see, one of the biggest challenges lies in understanding the ‘sentiments’ behind social media posts.

And this is the perfect idea for your next machine learning project!

Social media is thriving with tons of user-generated content. By creating an ML system that could analyze the sentiment behind texts, or a post, it would become so much easier for organizations to understand consumer behaviour. This, in turn, would allow them to improve their customer service, thereby providing the scope for optimal consumer satisfaction.

Must Read: Free deep learning course!

You can try to mine the data from Twitter or Reddit to get started off with your sentiment analyzing machine learning project. This might be one of those rare cases of deep learning projects which can help you in other aspects as well.

This sentiment analyzer project not only provides hands-on experience with NLP and machine learning but also aligns with practical applications for businesses. It is one of the most commonly pursued Machine Learning projects as it is highly relevant in the world of social media, helping to understand and respond to public sentiments on these platforms. The insights gained from this project can contribute to enhancing customer service strategies, brand management, and decision-making processes based on public sentiment analysis.

Source Code: Develop A Sentiment Analyzer

4. Enhance Healthcare

AI and ML applications have already started to penetrate the healthcare industry and are also rapidly transforming the face of global healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. They are not only helping HCPs (Health Care Providers) to deliver speedy and better healthcare services but are also reducing the dependency and workload of doctors to a significant extent.

So, why not use your skills to develop an impressive machine learning project based on healthcare? To handle a project with Machine Learning algorithms for beginners can be helpful to build your career with a good start.

These 6 Machine Learning Techniques are Improving Healthcare

The healthcare industry has enormous amounts of data at their disposal. By harnessing this data, you can create:

  • Diagnostic care systems that can automatically scan images, X-rays, etc., and provide an accurate diagnosis of possible diseases.
  • Preventative care applications that can predict the possibilities of epidemics such as flu, malaria, etc., both at the national and community level.

Source Code: Enhance Healthcare

5. Prepare ML Algorithms – From Scratch!

This is one of the excellent machine learning project ideas for beginners. Writing ML algorithms from scratch will offer two-fold benefits:

  • One, writing ML algorithms is the best way to understand the nitty-gritty of their mechanics.
  • Two, you will learn how to transform mathematical instructions into functional code. This skill will come in handy in your future career in Machine Learning.

You can begin by choosing an algorithm that is straightforward and not too complex. Behind the making of each algorithm – even the simplest ones – there are several carefully calculated decisions. Once you’ve achieved a certain level of mastery in building simple ML algorithms, try to tweak and extend their functionality. For instance, you could take a vanilla logistic regression algorithm and add regularization parameters to it to transform it into a lasso/ridge regression algorithm. Mentioning machine learning projects can help your resume look much more interesting than others.

Source Code: Prepare ML Algorithms

6. Develop A Neural Network That Can Read Handwriting

One of the best ideas to start experimenting you hands-on Java projects for students is working on neural network. Deep learning and neural networks are the two happening buzzwords in AI. These have given us technological marvels like driverless-cars, image recognition, and so on.
So, now’s the time to explore the arena of neural networks. Begin your neural network machine learning project with the MNIST Handwritten Digit Classification Challenge. It has a very user-friendly interface that’s ideal for beginners.

Machine Learning Engineers: Myths vs. Realities

It is additionally considered one of the easiest projects on machine learning, particularly for students exploring neural networks and deep learning with Java. It is an excellent entry point for students due to its simplicity, well-defined nature, and the availability of resources. It can help students foster a solid understanding of fundamental concepts that can be applied to more intricate Machine Learning projects for final year students in the future.

Source Code: Develop A Neural Network That Can Read Handwriting

7. Movie Ticket Pricing System

With the expansion of OTT platforms like Netflix, Amazon Prime, people prefer to watch content as per their convenience. Factors like Pricing, Content Quality & Marketing have influenced the success of these platforms.

The cost of making a full-length movie has shot up exponentially in the recent past. Only 10% of the movies that are made make profits. Stiff competition from Television & OTT platforms along with the high ticket cost has made it difficult for films to make money even harder. The rising cost of the theatre ticket (along with the popcorn cost) leaves the cinema hall empty.

Therefore, it is one of the best ML projects to choose from due to its industry relevance, the complexity of factors involved, the potential impact on business outcomes, and the opportunity for students to explore various aspects of predictive analytics and optimization in a real-world context.

An advanced ticket pricing system can definitely help the movie makers and viewers. Ticket price can be higher with the rise in demand for ticket and vice versa. The earlier the viewer books the ticket, the lesser the cost, for a movie with high demand. The system should smartly calculate the pricing depending on the interest of the viewers, social signals and supply-demand factors.

Source Code: Movie Ticket Pricing System

8. Iris Flowers Classification ML Project

One of the best ideas to start experimenting you hands-on Machine Learning projects for students is working on Iris Flowers classification ML project. Iris flowers dataset is one of the best datasets for classification tasks. Since iris flowers are of varied species, they can be distinguished based on the length of sepals and petals. This ML project aims to classify the flowers into among the three species – Virginica, Setosa, or Versicolor.

It is one of the most widely pursued ML project ideas because it is simple, easy to pursue and has high educational value. It’s a beginner-friendly introduction to classification problems, featuring a well-structured dataset of iris flower measurements. Learners can easily experiment with different classification algorithms, gaining hands-on experience in key ML concepts such as data preprocessing and model evaluation. The project’s straightforward nature makes it an ideal starting point, building a solid foundation for tackling more complex machine learning challenges.

This particular ML project is usually referred to as the “Hello World” of Machine Learning. The iris flowers dataset contains numeric attributes, and it is perfect for beginners to learn about supervised ML algorithms, mainly how to load and handle data. Also, since this is a small dataset, it can easily fit in memory without requiring special transformations or scaling capabilities. And this is the perfect idea for your next machine learning project!

You can download the iris dataset here.

Source Code: Iris Flowers Classification ML Project

9. BigMart Sales Prediction ML Project 

This is an excellent ML project idea for beginners. This ML project is best for learning how unsupervised ML algorithms function. The BigMart sales dataset comprises of precisely 2013 sales data for 1559 products across ten outlets in various cities. 

The aim here is to use the BigMart sales dataset to develop a regression model that can predict the sale of each of 1559 products in the upcoming year in the ten different BigMart outlets. The BigMart sales dataset contains specific attributes for each product and outlet, thereby helping you to understand the properties of the different products and stores that influence the overall sales of BigMart as a brand.

This project also stands out as one of the most common AI ML projects for final year students as well due to its real-world applicability and comprehensive learning opportunities. It involves predicting sales for different products in multiple stores, reflecting the complexities of retail scenarios. Both beginner-level and final year students can gain practical experience in handling large datasets, addressing missing values, and selecting relevant features for prediction. 

Moreover, the project allows for the exploration of regression algorithms, enabling students to understand how to predict continuous numerical values. The real-world relevance of sales prediction in retail makes it an engaging project, providing valuable insights into business analytics and decision-making. 

Source Code: BigMart Sales Prediction ML Project 

10. Recommendation Engines with MovieLens Dataset

Recommendation engines have become hugely popular in online shopping and streaming sites. For instance, online content streaming platforms like Netflix and Hulu have recommendation engines to customize their content according to individual customer preferences and browsing history. By tailoring the content to cater to the watching needs and preferences of different customers, these sites have been able to boost the demand for their streaming services.

As a beginner, you can try your hand at building a recommendation system using one of the most popular datasets available on the web – MovieLens dataset. This dataset includes over “25 million ratings and one million tag applications applied to 62,000 movies by 162,000 users.” You can begin this project by building a world-cloud visualization of movie titles to make a movie recommendation engine for MovieLens.

Creating a recommendation engine with the MovieLens Dataset is also considered one of the best Machine Learning project topics due to its practical relevance and broad learning scope. It allows students to explore collaborative filtering and content-based filtering, essential techniques in recommendation systems.

This project is not only instructive but also mirrors real-world applications on platforms like Netflix, offering students a valuable opportunity to understand and implement machine learning in personalized content recommendations.

You can check out the MovieLens dataset here.

Source Code: Recommendation Engines with MovieLens Dataset

11. Predicting Wine Quality using Wine Quality Dataset

It’s a well-established fact that age makes wine better – the older the wine, the better it will taste. However, age is not the only thing that determines a wine’s taste. Numerous factors determine the wine quality certification, including physiochemical tests such as alcohol quantity, fixed acidity, volatile acidity, density, and pH level, to name a few. 

In this ML project, you need to develop an ML model that can explore a wine’s chemical properties to predict its quality. The wine quality dataset you’ll be using for this project consists of approximately 4898 observations, including 11 independent variables and one dependent variable. Mentioning Machine Learning projects for the final year can help your resume look much more interesting than others.

Predicting wine quality using the Wine Quality Dataset is also an excellent machine learning project for beginners. The dataset is well-organized, allowing beginners to focus on key machine learning concepts like data exploration, preprocessing, and model evaluation.

This project offers hands-on experience in essential tasks like feature selection and model training while showcasing the practical use of machine learning in predicting real-world outcomes, making it an ideal choice for beginners seeking a straightforward introduction to machine learning.

Source Code: Predicting Wine Quality using Wine Quality Dataset

12. MNIST Handwritten Digit Classification 

This is one of the interesting machine learning projects. Deep Learning and neural networks have found use cases in many real-world applications like image recognition, automatic text generation, driverless cars, and much more. However, before you delve into these complex areas of Deep Learning, you should begin with a simple dataset like the MNIST dataset. So, why not use your skills to develop an impressive machine learning project based on MNIST?

The MNIST digit classification project is designed to train machines to recognize handwritten digits. Since beginners usually find it challenging to work with image data over flat relational data, the MNIST dataset is best for beginners. In this project, you will use the MNIST datasets to train your ML model using Convolutional Neural Networks (CNNs). Although the MNIST dataset can seamlessly fit in your PC memory (it is very small), the task of handwritten digit recognition is pretty challenging.

Regarded as one of the best projects on Machine Learning, it serves as an effective starting point for learners, providing a structured introduction to computer vision and neural networks.

You can access the MNIST dataset here.

Source Code: MNIST Handwritten Digit Classification

13. Human Activity Recognition using Smartphone Dataset

This is one of the trending machine learning project ideas. The smartphone dataset includes the fitness activity record and information of 30 people. This data was captured through a smartphone equipped with inertial sensors. 

This ML project aims to build a classification model that can identify human fitness activities with a high degree of accuracy. By working on this ML project, you will learn the basics of classification and also how to solve multi-classification problems.

This is one of those projects on Machine Learning that holistically offers a comprehensive and practical learning experience, making it an excellent choice for those seeking a project that combines real-world impact with a broad range of machine learning skills.

It mirrors real-world challenges seen in wearable tech and health monitoring with the help of signal processing, and feature engineering, and offers learners a well-rounded grasp of the entire machine learning process.

This practical application not only makes the project interesting but also prepares learners for the kind of complexities they might encounter in various industries, making it an excellent choice for those wanting hands-on experience with real-world data.

Source Code: Human Activity Recognition using Smartphone Dataset

14. Object Detection with Deep Learning

This is one of the interesting machine learning projects to create. When it comes to image classification, Deep Neural Networks (DNNs) should be your go-to choice. While DNNs are already used in many real-world image classification applications, this ML project aims to crank it up a notch.

In this ML project, you will solve the problem of object detection by leveraging DNNs. You will have to develop a model that can both classify objects and also accurately localize objects of different classes. Here, you will treat the task of object detection as a regression problem to object bounding box masks. Also, you will define a multi-scale inference procedure that can generate high-resolution object detections at a minimal cost. 

This is one of the best ML projects due to its broad applications in crucial industries like autonomous vehicles, surveillance, and image recognition. To implement this project successfully, learners delve into deep learning architectures such as Convolutional Neural Networks (CNNs) and use frameworks like TensorFlow or PyTorch.

It offers hands-on experience to learners includes preprocessing image data, fine-tuning pre-trained models, and optimizing for accuracy and efficiency. This project will provide the practical skills you need to apply while working in diverse domains of Machine Learning.

Source Code: Object Detection with Deep Learning

15. Fake News Detection

This is one of the excellent machine learning project ideas for beginners, especially how fake news are spreading like wildfire now. Fake news has a knack for spreading like wildfire. And with social media dominating our lives right now, it has become more critical than ever to distinguish fake news from real news events. This is where Machine Learning can help. Facebook already uses AI to filter fake and spammy stories from the feeds of users.

This ML project aims to leverage NLP (Natural Language Processing) techniques to detect fake news and misleading stories that emerge from non-reputable sources. You can also use the classic text classification approach to design a model that can differentiate between real and fake news. In the latter method, you can collect datasets for both real and fake news and create an ML model using the Naive Bayes classifier to classify a piece of news as fraudulent or real based on the words and phrases used in it.

Source Code: Fake News Detection

16. Enrol Email Project

The Enron email dataset contains almost 500k emails of over 150 users. It is an extremely valuable dataset for natural language processing. This project involves building an ML model that uses the k-means clustering algorithm to detect fraudulent actions. The model will separate the observations into ‘k’ number of clusters according to similar patterns in the dataset.

This dataset holds immense value for natural language processing applications. This project not only delves into the complexities of natural language processing but also addresses the crucial task of detecting fraudulent activities, making it a noteworthy and multifaceted undertaking in the realm of machine learning.

As one of the highest pursued ML project ideas, it offers a practical and comprehensive learning experience, making it an excellent choice for both beginners and final year students looking to engage with impactful real-world machine learning applications.

Source Code: Enrol Email Project

17. Parkinson’s project

The Parkinson dataset includes 195 biomedical records of people with 23 varied characteristics. The idea behind this project is to design an ML model that can differentiate between healthy people and those suffering from Parkinson’s disease. The model uses the XGboost (extreme gradient boosting) algorithm based on decision trees to make the separation.

XGBoost excels at handling complex relationships within the data, making it particularly suitable for medical diagnosis tasks. The model is trained on the dataset’s features, which likely include various biomedical measurements, and learns to distinguish patterns indicative of Parkinson’s disease.

Through Machine Learning project topics like these, learners can gain hands-on experience in feature engineering, model training, and the interpretability of decision tree-based algorithms. The ultimate goal of is to create a reliable predictive tool that aids in the early detection of Parkinson’s disease, showcasing the impactful intersection of machine learning and healthcare.

Source Code: Parkinson’s project

18. Flickr 30K project

The Flickr 30K dataset consists of more than 30,000 images, each having a unique caption. You will use this dataset to build an image caption generator. The idea is to build a CNN model that can effectively analyze and extract features from an image and create a befitting caption describing the image in English.

This project offers hands-on experience to learners in merging computer vision and natural language processing. By working on it, practitioners learn to prepare image data, create and train a CNN model, and seamlessly connect it with a natural language processing component for generating captions.

The primary goal is to develop an intelligent system capable of interpreting images and conveying their content through human-like captions. It highlights the intriguing blend of image analysis and language generation within the field of artificial intelligence.

Source Code: Flickr 30K project

19. Mall customers project

As the name suggests, the mall customers dataset includes the records of people who visited the mall, such as gender, age, customer ID, annual income, spending score, etc. You will build a model that will use this data to segment the customers into different groups based on their behavior patterns. Such customer segmentation is a highly useful marketing tactic used by brands and marketers to boost sales and revenue while also increasing customer satisfaction.

Machine learning models, particularly clustering algorithms like K-means, can be applied to achieve this segmentation. This project provides hands-on experience in data analysis and model building, offering insights into consumer behavior and helping businesses make informed marketing decisions. Ultimately, it demonstrates how machine learning can optimize marketing strategies and enhance the overall customer experience.

Source Code: Mall customers project

20. Kinetics project 

For this project, you will use an extensive dataset that includes three separate datasets – Kinetics 400, Kinetics 600, and Kinetics 700 – containing URL links of over 6.5 million high-quality videos. Your goal is to create a model that can detect and identify the actions of a human by studying a series of different observations.

This project with the Kinetics datasets is valuable for its real-world applications, like enhancing surveillance, improving human-computer interaction, and optimizing content recommendation systems. For instance, an action recognition model developed in this project could be used in video surveillance to automatically identify and categorize human activities.

Engaging in this project offers practical experience in managing extensive video datasets, preparing video data for analysis, and building sophisticated deep learning models. It also provides insights into the complexities of analyzing temporal data and the nuances of identifying human actions in videos.

Source Code: Kinetics project 

21. Recommendation system project 

This a rich dataset collection containing a diverse range of datasets gathered from popular websites like Goodreads book reviews, Amazon product reviews, social media, etc. Your goal is to build a recommendation engine (like the ones used by Amazon and Netflix) that can generate personalized recommendations for products, movies, music, etc., based on customer preferences, needs, and online behavior.

ML projects ideas like these are highly significant because they help develop skills like processing diverse data, implementing recommendation algorithms, and evaluating system performance. These skills are highly applicable across industries, highlighting the universal use of recommendation systems in the digital landscape.

Practically, the project provides hands-on experience and delves into ethical considerations like privacy and algorithmic bias. All in all, it’s a practical exploration of how machine learning enhances user interactions and decision-making in various domains.

Source Code: Recommendation system project

22. The Boston housing project

The Boston housing dataset consists of the details of different houses in Boston based on factors like tax rate, crime rate, number of rooms in a house, etc. It is an excellent dataset for predicting the prices of different houses in Boston. In this project, you will build a model that can predict the price of a new house using linear regression. Linear regression is best suited for this project since it is used where the data has a linear relationship between the input and output values and when the input is unknown.

Source Code: The Boston housing project

23. Cityscapes project

This open-source dataset includes high-quality pixel-level annotations of video sequences collected from the streets across 50 different cities. It is immensely useful for semantic analysis. You can use this dataset to train deep neural nets to analyze and understand the urban cityscape. The project involves designing a model that can perform image segmentation and identify various objects (cars, buses, trucks, trees, roads, people, etc.) from a street video sequence. 

Source Code: Cityscapes project

24. YouTube 8M project 

The Youtube 8M is a huge dataset that has 6.1 million YouTube video IDs, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and an average of 3 labels for each video. It is widely used for video classification projects. In this project, you will build a video classification system that can accurately describe a video. It will consider a series of different inputs and classify the videos into separate categories.

Source Code: YouTube 8M project 

25. Urban sound 8K

The urban sound 8K dataset is used for sound classification. It includes a diverse collection of 8732 urban sounds belonging to different classes such as sirens, street music, dog barking, birds chirping, people talking, etc. You will design a sound classification model that can automatically detect which urban sound is playing.

Source Code: Urban sound 8K

26. IMDB-Wiki project 

This labeled dataset is probably one of the most extensive collections of face images gathered from across IMDB and Wikipedia. It has over 5 million face images labeled with age and gender. with labeled gender and age. You will create a model that can detect faces and predict their age and gender with accuracy. You can make different age segments/ranges like 0-10, 10-20, 30-40, and so on. 

Source Code: IMDB-Wiki project 

27. Librispeech project

The librispeech dataset is a massive collection of English speeches derived from the  LibriVox project. It contains English-read speeches in various accents that span over 1000 hours and is the perfect tool for speech recognition. The focus of this project is to create a model that can automatically translate audio into text. You will build a speech recognition system that can detect English speech and translate it into text format. 

Source Code: Librispeech project

28. German traffic sign recognition benchmark (GTSRB) project

This dataset contains more than 50,000 images of traffic signs segmented into 43 classes and containing information on the bounding box of each traffic sign. It is ideal for multiclass classification which is exactly what you will focus on here. You will build a model using a deep learning framework that can recognize the bounding box of signs and classify traffic signs. The project can be extremely useful for autonomous vehicles as it detects signs and helps drivers take the necessary actions.

Source Code: German traffic sign recognition benchmark (GTSRB) project

29. Sports match video text summarization

This project is exactly as it sounds – obtaining an accurate and concise summary of a sports video. It is a useful tool for sports websites that inform readers about the match highlights. Since neural networks are best for text summarization, you will build this model using deep learning networks such as 3D-CNNs, RNNs, and LSTMs. You will first fragment a sports video into multiple sections by using the appropriate ML algorithms and then use a combination of SVM(Support vector machines), neural networks, and k-means algorithm.

Source Code: Sports match video text summarization

30. Business meeting summary generator

Summarization involves extracting the most meaningful and valuable bits of information from conversations, audio/video files, etc., briefly and concisely. It is generally done by feature capturing the statistical, linguistic, and sentimental traits with the dialogue structure of the conversation in question. In this project, you will use deep learning and natural language processing techniques to create precise summaries of business meetings while upholding the context of the entire conversation.

Source Code: Business meeting summary generation

31. Sentiment analysis for depression

Depression is a major health concern globally. Each year, millions of people commit suicide due to depression and poor mental health. Usually, the stigma attached to mental health problems and delayed treatment are the two main causes behind this. In this project, you will leverage the data gathered from different social media platforms and analyze linguistic markers in social media posts to understand the mental health of individuals. The idea is to create a deep learning model that can offer valuable and accurate insights into one’s mental health much earlier than conventional methods.

Source Code: Sentiment analysis for depression

32. Handwritten equation solver 

Handwritten mathematical expression recognition is a crucial field of study in computer vision research. You will build a model and train it to solve handwritten mathematical equations using Convolutional Neural Networks. The model will also make use of image processing techniques. This project involves training the model with the right data to make it adept at reading handwritten digits, symbols, etc., to deliver correct results for mathematical equations of different complexity levels.

Source Code: Handwritten equation solver 

33. Facial recognition to detect mood and recommend songs

It is a known fact that people listen to music based on their current mood and feelings. So, why not create an application that can detect a person’s mood by their facial expressions and recommend songs accordingly? For this, you will use computer vision elements and techniques. The goal is to create a model that can effectively leverage computer vision to help computers gain a high-level understanding of images and videos.

A good deep learning project is developing a facial recognition system, as it involves various complex tasks like image processing, feature extraction, and the application of convolutional neural networks (CNNs), providing a comprehensive learning experience.

Source Code: Facial recognition to detect mood and recommend songs

34. Music generator

A music composition is nothing but a melodious combination of different frequency levels. In this project, you will design an automatic music generator that can compose short pieces of music with minimal human intervention. You will use deep learning algorithms and LTSM networks for building this music generator.

Source Code: Music generator

35. Disease prediction system

This ML project is designed to predict diseases. You will create this model using R and R Studio and the Breast Cancer Wisconsin (Diagnostic) Dataset. This dataset includes two predictor classes – benign and malignant breast mass. It is essential to have a basic knowledge of random forests and XGBoost for working on this project.

Source Code: Disease prediction system

36. Finding a habitable exo-planet 

In the past decade, we’ve been successful in identifying many transiting and exo-planets. Since the manual interpretation of potential exoplanets is pretty challenging and time-consuming (not to forget, it is also subject to human error), it is best to use deep learning to identify exoplanets. This project aims to find out if there are any habitable exoplanets around us using CNNs and noisy time-series data. This method can identify habitable exoplanets with more precision than the least-squares method.

Source Code: Habitability of Exo-planets

37. Image regeneration for old & damaged reels

Restoring old or damaged picture reels is a challenging task. It is almost always impossible to restore old photos to their original state. However, deep learning can solve this problem. You will build a deep learning model that can identify the defects in an image (scuffs, holes, folds, decoloration, etc.) and using Inpainting algorithms to restore it. You can even colorize old B&W images.

Source Code: Restore old and damaged photos

38. Loan Eligibility Prediction

Loans are currently the core business especially for banks because their key profit derives from the interest levied on loans. Generally, economic growth is guaranteed when individuals put some part of their money into some business with the hope that it could multiply in the future. Although it comes with risk, sometimes it becomes inevitable to take a loan. Because loans contribute to one of the most important components of our lives, loan eligibility prediction can be greatly beneficial. Therefore, it is one of the important ML mini projects. Moreover, it is among those ML projects with great influence on various sectors.

The model for evaluating the loan eligibility prediction needs to be trained through a dataset that comprises data including data. Examples of data can be marital status, gender, income, credit card history, loan amount, etc. Moreover, this machine learning idea guarantees better planning in addition to the loan being accepted or rejected. If you are looking for some AI ML projects for final year, this could be a great opportunity.

Source Code: Loan Eligibility Prediction

39. Inventory Demand Forecasting

Zomato is a famous mobile app in India that connects customers to neighboring food chains by offering them their delivery persons. Preparing enough inventories is a responsibility that Zomato and the registered restaurants have to complete. The majority of the companies that provide need to ascertain that they have sufficient stock to meet their customers’ expectations. Therefore, it becomes vital to get a rough approximation of how much preparation is required. You can achieve this preparation using one of the valuable ML projects for beginners i.e. Inventory Demand Forecasting.

The corresponding predictions in demand forecasting could be accomplished using the application of corresponding ML algorithms. Moreover, these ML projects for beginners can be executed by using ML algorithms like Boosting, Bagging, Gradient Boosting Machine (GBM), XGBoost, Support Vector Machines, and more.

Source Code: Inventory Demand Forecasting

40. Customer Churn Prediction Analysis Using Ensemble Techniques in Machine Learning

This is one of the best Machine Learning projects. Customers are the greatest asset of any company. Retaining customers is vital to enhance revenue and develop a lasting relationship with them. Furthermore, acquiring new customers is approximately five times more expensive than retaining a prevailing customer.

One of the prevalent ML mini projects when it comes to predicting customers’ churn is the “Customer Churn Prediction Analysis Using Ensemble Techniques in Machine Learning”. For this project idea, the question is how to begin solving the customer churn rate prediction ML problem. Like other ML problems, machine learning engineers or data scientists must gather and prepare the relevant data for processing. Moreover, it must use data engineering in the proper format to ensure effectiveness.

It is important to note that for these ML mini projects, Feature Engineering is the greatest creative aspect of the churn prediction ML model. It implies that data specialists apply their domain knowledge of the data, business context, experience, and creativity to design features. Also, these aspects help to personalize the ML model to comprehend why customer churn takes place in a business.

Source Code: Customer Churn Prediction Analysis

 41. Predict Credit Default -Credit Risk Prediction Project

For MBA or management course students, this one is one of the important machine learning projects for final year. It aims to predict customers who would default on a loan. When implementing this project idea, the banks may encounter losses on credit card products from different sources. One probable reason for this loss is whenever the customers default on the loan, their debt prevents banks from collecting the payments for the offered services.

In these types of machine learning projects for final year, you will scrutinize a group of the customer database to determine the number of customers seriously aberrant in paying in the subsequent 2 years. Various ML models are available to predict which customers default on a loan. Based on this information, the banks can cancel the credit lines for precarious customers or reduce the credit limit issued on the card to reduce losses.

Source Code: Predict Credit Default -Credit Risk Prediction Project

 42. Predicting Interest Levels of Rental Listings

We all want to comfortably lie in our homes after working for long hours at the workplace. The pandemic has revamped the work culture and facilitated work from home culture. So, the significance of finding a comfortable house has increased.

This project idea performs a sentimental investigation on the viewers for different rental listings. It becomes easy to evaluate their reactions to specific houses. Accordingly, it becomes easy to determine the popularity of those houses available for rent. Furthermore, it can predict the interest rates of new locations yet to be listed.

Source Code: Predicting Interest Levels of Rental Listings

 43. Driver Demand Prediction

Food delivery services and ride-sharing worldwide depend on the drivers’ availability. This is an easy-to-use ML project for beginners that predicts the driver demand by transforming a time series problem into a controlled machine learning problem. Moreover, exploratory analysis needs to be carried out on the time series to recognize patterns. Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF) will be employed to evaluate the time series. Furthermore, this project idea implies building the regression model to solve the time-series problem.

Source Code: Driver Demand Prediction

 44. Market Basket Analysis

In terms of customer purchase patterns, Market Basket Analysis is one of the valuable machine learning based projectsIt understands the combinations in which the customers usually purchase different commodities. Moreover, it is somewhat similar to the AI ML projects because it uses a data mining technique that observes purchasing patterns of consumers to understand them and eventually boost sales effectively.

This project idea is such that if a customer buys an item(s), it raises the chances of buying another item(s). The interest in other items (s) is based on the purchasing behaviors of former customers. Furthermore, this project idea is used for targeted promotions and to provide customers with tailored recommendations.

Source Code: Market Basket Analysis

45. Production Line Performance Checker

Leading engineering and technology companies, for example, Bosch deals with various business sectors like consumer goods, industrial technology, etc. One of the greatest challenges such companies face is to keep track of the manufacturing of the companies’ mechanical modules.

One of the most practical machine learning based projects is the Production Line Performance Checker. Like AI ML projects, this one also uses the latest technologies to predict the failures in the components’ production over the assembly line. It faces a challenge while implementing the analytical techniques because the production lines are usually complex, and the data may not be analyst-friendly. This challenge makes this machine learning project idea interesting.

Source Code: Production Line Performance

Real-world industry projects 

Magenta

This research project focuses on exploring the applications of machine learning in the creation process of art and music. You will develop unique reinforcement learning and deep learning algorithms that can generate images, songs, music, and much more. It is the perfect project for creative minds passionate about art and music. 

BluEx

BluEx is among the leading logistics company in India that has developed quite a fanbase, thanks to its timely and efficient deliveries. However, as is true of all logistics providers, BluEx faces one particular challenge that costs both time and money – its drivers do not frequent the optimal delivery paths which causes delays and leads to higher fuel costs. You will create an ML model using reinforcement learning that can find the most efficient path for a particular delivery location. This can save up to 15% of the fuel cost for BluEx. 

Motion Studios

Motion Studios boasts of being Europe’s largest Radio production house with revenue exceeding a billion dollars. Ever since the media company launched their reality show, RJ Star, they’ve received a phenomenal response and are flooded with voice clips. Being a reality show, there’s a limited time window for choosing candidates. You will build a model that can differentiate between male and female voices and classify voice clips to facilitate quicker filtration. This will help is faster selection, easing the task of the show executives. 

LithionPower

Lithionpower builds batteries for electric vehicles. Usually, drivers rent the company’s batteries for a day and replace them with a charged battery. The battery life depends on factors like distance driven/day, overspeeding, etc. LithionPower employs a variable pricing model based on a driver’s driving history. The goal of this project is to build a cluster model that will group drivers according to their driving history and incentivize drivers based on those clusters. While this will increase profits by 15-20%, it will also charge more from drivers having a poor driving history. 

Steps to Keep in Mind to Complete a Machine Learning Project for Beginners – 

You must adhere to a set of established procedures when working on AI and ML projects. For each initiative, we must first gather the information in accordance with our operational requirements. The following stage is to clean the data, which includes deleting values, addressing outliers, handling unbalanced datasets, and converting them to a numeric value, among other things. There are different algorithms that you can follow to create the best machine learning projects. 

  • Gathering Data 

When collecting data for AI ML projects, it is necessary to ask certain questions yourself. For example, what is the problem you are trying to solve? Are there previously existing data sources? Is the data publicly available? 

When talking about structured data, they can be of different types, like, as categorical, numerical, and ordinal. 

  1. Categorical data – Categorical data in AI ML projects refers to the data that is collected based on the name, age, sex, or even hair colour. For example, when selling a car, there are several categories, like colour, type of wheel, etc. 
  2. Numerical – Any data that is collected in the form of numbers is called numerical data. It is also known as quantitative data. For example, if you are selling a house, the numerical data would be the price or the surface area. 
  3. Ordinal – Ordinal data in AI ML projects  refers to a set order or scale is used with ordinal data, which is a type of categorical data. For example, using a scale of 1-10, a person’s response indicates their level of financial happiness. 
  • Preparing the Data 

The act of data preparation for AI and ML projects involves gathering the information you need, converting it to a computer-readable format, and testing its accuracy and bias by asking hard questions about it. 

Instead of concentrating exclusively on the data of the AI ML projects for beginners, take into account the problem you’re attempting to solve. That could make decisions regarding the sort of data to collect, how to make sure it serves the main objective, and how to structure it appropriately for a particular sort of algorithm easier to make.

In addition to allowing them to adjust to model performance drifts and changes in direction to data analytical challenges, good information preprocessing may result in more precise and effective methods and ultimately spare data analysts and entrepreneurs a great deal of time and effort. This could help you prepare AI ML projects for beginners. 

  • Evaluation of Data 

Plans for evaluation of best ML projects should include where, how, and from what sources data is gathered. The structure used to gather both quantitative (numerical) and qualitative data must keep up with performance objectives, project schedules, and programme goals. 

  • Model Production

This is one of the most important steps in preparing for AI ML projects for beginners. This step helps you determine how the model is performing. To make sure that the testing is fine, you may use machine learning tools like PyTorch Serving, Sagemaker, Google AI Platform, and more. You can also use MLOps (a mixture of machine learning and software engineering), which includes all the technologies that are required to make sure that the machine learning model works just fine. This is also an important step when making AI ML projects for final year. 

Conclusion

Here is a comprehensive list of machine learning project ideas. Machine learning is still at an early stage throughout the world. There are a lot of projects to be done, and a lot to be improved. With smart minds and sharp ideas, systems with support business get better, faster and profitable. If you wish to excel in  Machine Learning, you must gather hands-on experience with such machine learning projects.

You can also check our Executive PG Programme in Machine Learning & AI from IIT DelhiIIT Delhi is one the most prestigious institutions in India. With more the 500+ In-house faculty members which are the best in the subject matters.

Only by working with ML tools and ML algorithms can you understand how ML infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our machine learning project ideas guide to build your very own machine learning projects!

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Frequently Asked Questions (FAQs)

1. How easy it is to implement these projects?

These projects are very basic, someone with a good knowledge of Machine Learning can easily manage to pick and finish any of these projects.

2. Can I do this projects on ML Internship?

Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.

3. Why do we need to build machine learning projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.

4. How to start my first machine learning project?

Every machine learning project starts with a thorough comprehension of the data and the development of goals. You are studying, building, and analyzing the data as you apply machine learning techniques to your data set to reach the end result. The steps for developing a well-defined ML project are as follows: recognize and define the issue, analyze and prepare the data, make use of the algorithms, errors should be reduced, and predict the outcome.

5. How do I start my first deep learning project?

To develop a model, Deep Learning (DL) requires millions of iterations. It's difficult to find bugs, and it breaks easily. Begin with something easy and progress from there. Regularization and other model optimizations can always be done once the code has been debugged. Visualize your model metrics and forecasts on a regular basis. Make something that works first so you can fall back on it. Do not become engrossed with a large model. It's more enjoyable to observe improvement. Many AI initiatives are lighthearted and entertaining. For example, you can start a project to colorize Japanese Manga in early 2017 as part of a research on the topic of Generative Adversarial Network (GAN).

6. What is the future of machine learning?

Machine learning has a bright future ahead of it. Machine learning applications are being used in practically every mainstream domain. Healthcare, search engines, digital marketing, and education, to name a few, are all important beneficiaries. It indicates that achieving goal results in a domain devoid of this new technology is nearly impossible. Machine Learning may be a debatable benefit to a business or organization, whether it is a multinational corporation or a start-up, because jobs that are currently completed manually will be entirely completed by machines in the future.

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Jaideep Khare

Jaideep is in the Academics & Research team at UpGrad, creating content for the Data Science & Machine Learning programs. He is also interested in the conversation surrounding public policy related to AI.

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“Remember that dystopian view of the future in which technology displaces millions of people from their jobs? It’s happening” Jeff Weiner, CEO LinkedIn, wrote when Microsoft announced it was acquiring LinkedIn. Some of the top companies in the world such as handset maker Foxconn, US-based retail company Walmart and McDonald’s are now turning to robots and automation. It’s true that some jobs may become defunct as this shift becomes more pronounced. At the same time, these technologies doubtless offer lots of opportunities for many other types of jobs such as digital curation and preservation, data mining and big data analytics. 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!

7.87K+

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

6.14K+

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

8.3K+

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

10.99K+

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

20.1K+

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

5.82K+

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