Jaideep Khare

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Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorithms – From Scratch! Develop A Neural Network That Can Read Handwriting Movie Ticket Pricing System Iris Flowers Classification ML Project BigMart Sales Prediction ML Project Recommendation Engines with MovieLens Dataset Predicting Wine Quality using Wine Quality Dataset MNIST Handwritten Digit Classification Human Activity Recognition using Smartphone Dataset Object Detection with Deep Learning 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. 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 2. SportsPredictor 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. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 3. 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. 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. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 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. 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 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. 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. 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. 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. 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.  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. You can check out the MovieLens dataset here. 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. 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. You can access the MNIST dataset here. 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. 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.  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. 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. 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. 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. 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.  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. 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. 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. 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.  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. 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 in the 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.  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.  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.  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.  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.  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.  44. Market Basket Analysis In terms of customer purchase patterns, Market Basket Analysis is one of the valuable machine learning based projects.  It 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. 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. 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.  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 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.  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.  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.  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 Delhi. IIT 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! Refer to your Network! If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to ₹70,000/- You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

Deep learning is a subset of Machine learning concerned with algorithms inspired by the brain’s structure and function, known as artificial neural networks. Although it is an emerging field, deep learning applications are rapidly gaining popularity in the industry.  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 Making a profession in deep learning is highly promising for individuals, as there is an ever-expanding scope of employment opportunities across multiple industries like BFSI, hospitality, retail, manufacturing, energy, cybersecurity, and agriculture, like Food and Drug Administration (FDA), Pinterest, Facebook- chatbot army, Twitter, Google – Neural Networks and Machines That Dream, Edgecase, Baidu, and HubSpot to name a few.   While acquiring theoretical knowledge is vital to begin your deep learning journey, it isn’t enough – you must also be capable of putting your knowledge to the test. If you wish to carve a niche for yourself in the deep learning domain,  you must build a highly refined skill set.  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. The best way to go about upskilling is to enrol in online deep learning courses and certification programs. Since these programs have a standardised curriculum and a hands-on approach to learning, learners can gain theoretical and practical skills. Here, we have prepared a list of the best online deep learning courses for you that are available and are brought to you by: upGrad Linkedin Microsoft Deep Learning Courses upGrad provides you with a range of industry-specific courses ranging from Data Science and Deep Learning to DBA, MBA, and Business analytics 1. Executive PG Program in Machine Learning and Deep Learning with IIIT B upGrad offers this online course that’ll help you master exploratory data analysis, regression analysis, unsupervised learning, neural networks, gesture recognition & much more in just six months!  The course is designed for working professionals. It includes 5+ industry projects along with case studies and assignments. The course covers topics like exploratory data analysis, regression analysis, unsupervised learning, neural networks, gesture recognition, and much more.  Each learner enjoys one-on-one personalized mentorship from industry experts and trained instructors. Not just that, students also get 360-degree career support from a dedicated student success mentor and placement assistance. Minimum Eligibility A bachelor’s degree with 50% or equivalent passing marks. Who is this Course For? Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals. 2. Executive PG Program in Machine Learning and Natural Language Processing with IIIT B upGrad offers this online course that’ll help you master Naive Bayes, Tree Models, Unsupervised Learning, Lexical, Syntactic & Semantic Processing, Building a Chatbot within just six months. This course is designed for working professionals, and it includes industry projects, case studies, and projects. The course covers topics like Naive Bayes, tree models, unsupervised learning, lexical, syntactic & semantic processing, building a chatbot, and much more. Each learner enjoys one-on-one personalised mentorship from industry experts and trained instructors. This course also promises 360-degree career support and placement assistance. Minimum Eligibility Bachelor’s degree with 50% or equivalent passing marks. Who is this Course For? Data Scientist, Data Engineer, Machine Learning Engineer, Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals. 3. Executive PG Program in Machine Learning and Artificial Intelligence with IIIT Bangalore upGrad offers this online course that’ll help you master Data Science Tool Kit, Statistics and Exploratory Data Analysis, Machine Learning, Natural Language Processing, Deep Learning, Reinforcement Learning, and Deployment and Capstone Projects in just 12 months! This upGrad course is designed for working professionals, and it includes 30+ case studies and assignments. Apart from personalised mentorship, you will also enjoy 360-degree career support and placement assistance from upGrad. Minimum Eligibility Bachelor’s degree with 50% or equivalent passing marks. Who is this Course For? Data Scientist, Data Engineer, Machine Learning Engineer, and Software Engineers. On course completion, you will receive a PG certificate from IIIT Bangalore for each of these courses. These certifications will make you ready for roles like Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist, and Software Engineer. Apart from upGrad, you can also try data analytics courses offered by LinkedIn, Microsoft, and Google AI.  4. Applied Machine Learning: Algorithms Online Class (LinkedIn) This course is divided into two instalments, both facilitated by Derek Jedamski, a skilled Data Scientist specialising in Machine Learning. The course also includes seven-chapter quizzes to test your knowledge. The first instalment in the two-part Applied Machine Learning series, the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalises to unobservable examples. Instead of zeroing in on any specific machine learning algorithm, the focus of the course is on giving the tools to solve nearly any kind of machine learning problem efficiently. The second part of the series covers the architecture by exploring various algorithms, from logistic regression to gradient boosting, and teaches the setting up a structure that acts as a guide by picking the best one for the problem at hand. 5. Master the Fundamentals of AI and Machine Learning Learning Path (LinkedIn)  After this course, you’ll have expertise over the concepts and future directions of technologies like artificial intelligence and machine learning. You’ll be well equipped to make well-informed decisions and contributions in your work environment. This course will help you gain a clear and detailed understanding of how AI and machine learning work and help you in learning how leading companies are using AI and machine learning to change their way of business and how the next generation of thinking about AI is addressing issues of accountability, security, and explainability. This course includes nine topics, these are:- AI Accountability Essential Training Artificial Intelligence Foundations: Machine Learning Artificial Intelligence Foundations: Thinking Machines Artificial Intelligence Foundations: Neural Networks Cognitive Technologies: The Real Opportunities For Business AI The LinkedIn Way: A COnversation With Deepak Agarwal Artificial Intelligence For Project Managers Learning XAI: Explainable Artificial Intelligence Artificial Intelligence For Cybersecurity  6. Create machine learning models – Learn (Microsoft) Machine learning is the foundation for predictive modelling and AI. Learning the core principles of machine learning and how to use the standard tools and frameworks to train, evaluate, and use machine learning models.  To be eligible for this course, one is required to have knowledge of basic mathematical concepts. Some experience with Python is also beneficial. The course is divided into five modules, these are: Explore and analyse data with Python Train and evaluate regression models Train and evaluate classification models Train and evaluate clustering models Train and evaluate deep learning models 7. Google Developers Machine Learning Crash Course (Google AI) This course is a self-study guide for aspiring machine learning practitioners. This course is beginner-friendly. This machine learning crash course features a series of lessons with video lectures. These lectures are conducted by google researchers, real-world case studies, and hands-on practice exercises. It has 25 lessons and over 30 exercises. Machine learning crash course doesn’t require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, it is recommended that students meet the following prerequisites: You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means. You should have some experience programming in Python because the programming exercises are in Python.  8. Advanced Certificate Programme in Machine Learning with IIIT Delhi The 7-month course offers students an opportunity to build conceptual knowledge of machine learning subjects like Deep Learning, Unsupervised Learning, Supervised Learning, Large Scale Machine Learning, Querying and Indexing, and Data Streams. It covers interactive lectures and weekly doubt clearing sessions by the world-renowned IITD faculty who expose students to industry-relevant case studies and projects to develop practical competence in designing and implementing ML algorithms.  Minimum eligibility Applicants should have a minimum of a Bachelor’s degree in Engineering, Science or Commerce with 50% aggregate passing marks.  Having a background in programming will help clear upGrad’s 40-minute entrance test with ease. Take the test now to evaluate your score and eligibility.  Who is this course for? Freshers  Mid-Level Managers (1 to 10 years of experience) Senior Executives (10+ years of experience) Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion The future is artificial intelligence and machine learning. No wonder why companies are always on the lookout for machine learning professionals who can help them take the lead, leaving their competitors behind. Acquiring machine learning and deep learning skills will enable you to fit in the industry that’s continually experimenting with new-age technologies. Not to forget, you will also earn hefty annual packages! If you’re interested to learn more about Deep Learning Techniques, machine learning, check out IIIT-B & upGrad’s Executive PG Program in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms. If you’re interested in expanding your skills and knowledge about deep learning, we hope this list of best deep learning courses offers you a sense of direction!

Welcome to our credit card fraud detection project. Today, we’ll use Python and machine learning to detect fraud in a dataset of credit card transactions. Although we have shared the code for every step, it would be best to understand how each step works and then implement 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 Let’s begin! Credit Card Fraud Detection Project With Steps In our credit card fraud detection project, we’ll use Python, one of the most popular programming languages available. Our solution would detect if someone bypasses the security walls of our system and makes an illegitimate transaction.   The dataset has credit card transactions, and its features are the result of PCA analysis. It has ‘Amount’, ‘Time’, and ‘Class’ features where ‘Amount’ shows the monetary value of every transaction, ‘Time’ shows the seconds elapsed between the first and the respective transaction, and ‘Class’ shows whether a  transaction is legit or not.  In ‘Class’, value 1 represents a fraud transaction, and value 0 represents a valid transaction.  You can get the dataset and the entire source code here.  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. Step 1: Import Packages We’ll start our credit card fraud detection project by installing the required packages. Create a ‘main.py’ file and import these packages: import numpy as np import pandas as pd import sklearn from scipy.stats import norm from scipy.stats import multivariate_normal from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt import seaborn as sns Step 2: Look for Errors Before we use the dataset, we should look for any errors and missing values in it. The presence of missing values can cause your model to give faulty results, rendering it inefficient and ineffective. Hence, we’ll read the dataset and look for any missing values: df = pd.read_csv(‘creditcardfraud/creditcard.csv’) # missing values print(“missing values:”, df.isnull().values.any()) We found no missing values in this dataset, so we can proceed to the next step.  Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Step 3: Visualization In this step of our credit card fraud detection project, we’ll visualize our data. Visualization helps in understanding what our data shows and reveals any patterns which we might have missed. Let’s create a plot of our dataset:  # plot normal and fraud count_classes = pd.value_counts(df[‘Class’], sort=True) count_classes.plot(kind=’bar’, rot=0) plt.title(“Distributed Transactions”) plt.xticks(range(2), [‘Normal’, ‘Fraud’]) plt.xlabel(“Class”) plt.ylabel(“Frequency”) plt.show() In our plot, we found that the data is highly imbalanced. This means we can’t use supervised learning algorithms as it will result in overfitting. Furthermore, we haven’t figured out what would be the best method to solve our problem, so we’ll perform more visualisation. Use the following to plot the heatmap:  # heatmap sns.heatmap(df.corr(), vmin=-1) plt.show() Now, we’ll create data distribution graphs to help us understand where our data came from:  fig, axs = plt.subplots(6, 5, squeeze=False) for i, ax in enumerate(axs.flatten()):    ax.set_facecolor(‘xkcd:charcoal’)    ax.set_title(df.columns[i])    sns.distplot(df.iloc[:, i], ax=ax, fit=norm,                 color=”#DC143C”, fit_kws={“color”: “#4e8ef5”})    ax.set_xlabel(”) fig.tight_layout(h_pad=-1.5, w_pad=-1.5) plt.show() With data distribution graphs, we found that nearly every feature comes from Gaussian distribution except ‘Time’.  So we’ll use multivariate Gaussian distribution to detect fraud. As only the ‘Time’ feature comes from the bimodal distribution (and note gaussian distribution), we’ll discard it. Moreover, our visualisation revealed that the ‘Time’ feature doesn’t have any extreme values like the others, which is another reason why we’ll discard it.  Add the following code to drop the features we discussed and scale others:  classes = df[‘Class’] df.drop([‘Time’, ‘Class’, ‘Amount’], axis=1, inplace=True) cols = df.columns.difference([‘Class’]) MMscaller = MinMaxScaler() df = MMscaller.fit_transform(df) df = pd.DataFrame(data=df, columns=cols) df = pd.concat([df, classes], axis=1) Step 4: Splitting the Dataset Create a ‘functions.py’ file. Here, we’ll add functions to implement the different stages of our algorithm. However, before we add those functions, let’s split our dataset into two sets, the validation set and the test set.  import pandas as pd import numpy as np def train_validation_splits(df):    # Fraud Transactions    fraud = df[df[‘Class’] == 1]    # Normal Transactions    normal = df[df[‘Class’] == 0]    print(‘normal:’, normal.shape[0])    print(‘fraud:’, fraud.shape[0])    normal_test_start = int(normal.shape[0] * .2)    fraud_test_start = int(fraud.shape[0] * .5)    normal_train_start = normal_test_start * 2    val_normal = normal[:normal_test_start]    val_fraud = fraud[:fraud_test_start]    validation_set = pd.concat([val_normal, val_fraud], axis=0)    test_normal = normal[normal_test_start:normal_train_start]    test_fraud = fraud[fraud_test_start:fraud.shape[0]]    test_set = pd.concat([test_normal, test_fraud], axis=0)    Xval = validation_set.iloc[:, :-1]    Yval = validation_set.iloc[:, -1]    Xtest = test_set.iloc[:, :-1]    Ytest = test_set.iloc[:, -1]    train_set = normal[normal_train_start:normal.shape[0]]    Xtrain = train_set.iloc[:, :-1]    return Xtrain.to_numpy(), Xtest.to_numpy(), Xval.to_numpy(), Ytest.to_numpy(), Yval.to_numpy() Step 5: Calculate Mean and Covariance Matrix The following function will helps us calculate the mean and the covariance matrix: def estimate_gaussian_params(X):    “””    Calculates the mean and the covariance for each feature.    Arguments:    X: dataset    “””    mu = np.mean(X, axis=0)    sigma = np.cov(X.T)    return mu, sigma FYI: Free nlp course! Step 6: Add the Final Touches In our ‘main.py’ file, we’ll import and call the functions we implemented in the previous step for every set: (Xtrain, Xtest, Xval, Ytest, Yval) = train_validation_splits(df) (mu, sigma) = estimate_gaussian_params(Xtrain) # calculate gaussian pdf p = multivariate_normal.pdf(Xtrain, mu, sigma) pval = multivariate_normal.pdf(Xval, mu, sigma) ptest = multivariate_normal.pdf(Xtest, mu, sigma) Now we have to refer to the epsilon (or the threshold). Usually, it’s best to initialise the threshold with the pdf’s minimum value and increase with every step until you reach the maximum pdf while saving every epsilon value in a vector. After we create our required vector, we make a ‘for’ loop and iterate over the same. We compare the threshold with the pdf’s values that generate our predictions in every iteration.  We also calculate the F1 score according to our ground truth values and the predictions. If the found F1 score is higher than the previous one, we override a ‘best threshold’ variable.  Keep in mind that we can’t use ‘accuracy’ as a metric in our credit card fraud detection project. That’s because it would reflect all the transactions as normal with 99% accuracy, rendering our algorithm useless.  We’ll implement all of the processes we discussed above in our ‘functions.py’ file: def metrics(y, predictions):    fp = np.sum(np.all([predictions == 1, y == 0], axis=0))    tp = np.sum(np.all([predictions == 1, y == 1], axis=0))    fn = np.sum(np.all([predictions == 0, y == 1], axis=0))    precision = (tp / (tp + fp)) if (tp + fp) > 0 else 0    recall = (tp / (tp + fn)) if (tp + fn) > 0 else 0    F1 = (2 * precision * recall) / (precision +                                     recall) if (precision + recall) > 0 else 0    return precision, recall, F1 def selectThreshold(yval, pval):    e_values = pval    bestF1 = 0    bestEpsilon = 0    for epsilon in e_values:        predictions = pval < epsilon        (precision, recall, F1) = metrics(yval, predictions)        if F1 > bestF1:            bestF1 = F1            bestEpsilon = epsilon    return bestEpsilon, bestF1 In the end, we’ll import the functions in the ‘main.py’ file and call them to return the F1 score and the threshold. It will allow us to evaluate our model on the test set: (epsilon, F1) = selectThreshold(Yval, pval) print(“Best epsilon found:”, epsilon) print(“Best F1 on cross validation set:”, F1) (test_precision, test_recall, test_F1) = metrics(Ytest, ptest < epsilon) print(“Outliers found:”, np.sum(ptest < epsilon)) print(“Test set Precision:”, test_precision) print(“Test set Recall:”, test_recall) print(“Test set F1 score:”, test_F1) Here are the results of all this effort:  Best epsilon found: 5e-324 Best F1 on cross validation set: 0.7852998065764023 Outliers found: 210 Test set Precision: 0.9095238095238095 Test set Recall: 0.7764227642276422 Test set F1 score: 0.837719298245614 Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion There you have it – a fully functional credit card fraud detection project! If you have any questions or suggestions regarding this project, let us know by dropping a comment below. We’d love to hear from you.  With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of the Execitive PG Program in Machine Learning & AI and talk to our career counsellor for more information.

The emergence of Big Data has given birth to one of the most lucrative careers of the 21st century – the Data Scientist. The term ‘Data Scientist’ has been making headlines for quite some time now. In fact, Data Scientist is one amongst the top 3 job positions on LinkedIn. The above fact speaks volume to strengthen the fact that professionals from various backgrounds – Mathematics, Computers, Management, Statistics – are looking to make the most out of this opportunity. But as with everything that gets thrown around a lot, the term ‘Data Science’, and therefore the job of a Data Scientist, has become largely vague. So, before we talk about the topic at hand, let’s look at what is it that a Data Scientist does. What does a Data Scientist do In simple words, a Data Scientist is an expert professional who deals extensively with Big Data. Data Scientists use a combination of Machine Learning, Artificial Intelligence, Statistics, and analytical tools to extract meaningful information from massive datasets. Unlike before, when datasets were mostly structured, the data at our disposal today is largely unstructured. So, naturally, Data Scientists spend a significant amount of their time in gathering, cleaning, and munging the data to enable its analysis and interpretation. Check out our data science certifications to upskill yourself The job role of a Data Scientist involves an amalgamation of mathematical, statistical, analytical, and programming skills. On any typical working day, a Data Scientist dons on many diverse roles throughout the entire course of the day – from being a Software Engineer and Data Miner to a Data Analyst and Troubleshooter, a Data Scientist also acts as the vital communication link between the IT and the business domains of a data-driven enterprise. It is Data Scientists who help Business Analysts to use the interpreted data in ways that can optimize business benefits. To be precise, Data Scientists help companies manage and interpret data to solve complex business problems. If you can picture yourself dealing with Big Data and performing such varied duties in the future, the job of a Data Scientist is your professional calling! However, to become a Data Scientist, you must first acquire the essential skills that are intrinsic to this profession. Like we mentioned before, Data Science demands specific skills. Thus, to become a Data Scientist, you must bear the following set of skills: Flair in programming To become a Data Scientist, the first rule is to have an impeccable knack for programming. So, you’ll have to have a solid knowledge of both statistical programming languages like Python or R or Java, and database querying languages like SQL, CQL, and so on. Companies, too, look for applicants who have command over at least two or more than two programming languages. Knowledge of Multivariable Calculus & Linear Algebra You may wonder why would a Data Scientist need to master Multivariable Calculus & Linear Algebra. It’s simply because having a solid understanding of Multivariable Calculus & Linear Algebra is immensely beneficial for data-driven organisations where even a minor alteration/improvement in algorithm optimization can deliver groundbreaking business opportunities. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Familiarity with the basics of Statistics A big part of the job of a Data Scientist requires dealing in Statistics. Every aspiring Data Scientist must have in-depth knowledge about statistical concepts like Descriptive Statistics (mean, median, range, standard deviation, etc.), Probability Theory, Bayes Theorem, Exploratory Data Analysis, Percentiles and Outliers, Random Variables, Cumulative Distribution Function (CDF), to name a few. The better you understand these concepts, the better you’ll be able to predict the validity of statistical approaches. An understanding of Artificial Intelligence (AI) and Machine Learning (ML) AI and ML ate two integral parts of Data Science, and hence, proficiency in these is a must. Surprisingly enough, not many Data Scientists are well-versed in AI and ML concepts and techniques. So, if you wish to stay ahead of the competitive curve, you better brush up on AI and ML concepts including Supervised ML, Unsupervised ML, Reinforcement Learning, Natural Language Processing (NLP), Recommendation engines, Outlier detection, and Survival analysis, among other things. Also, if you are proficient with ML techniques like decision trees, logistic regression, k means clustering, Naïve Bayes classifier algorithm, etc.,  you can solve a host of Data Science problems. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Our learners also read: Learn Python Online for Free Interests in Data Wrangling Data Scientists often deal with large, unstructured/semi-structured datasets that only keeps on increasing by the minute. As a result, they have to put a lot of effort into organising and cleaning the messy and complex datasets to enable easy analysis and interpretation. This process is known as Data Wrangling. What Data Scientists do is that they manually convert or map data from one raw format into another more convenient format, so that it becomes easy to keep the data organized and appropriate for interpretation and analysis. Therefore, as an aspiring Data Scientist, you must know how to deal with imperfections and glitches in data. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4   Knowledge of Data Visualization For professionals handling the business side of a company, it is difficult to make sense of raw data. This is where Data Scientists act as a crucial link between the IT and the business wings. After analysing and interpreting the data, Data Scientists visualize the data with the help of data visualization tools like Tableau, Matplottlib, ggplot, and d3.js. Further, they communicate their findings to both technical and non-technical staff for their ease of understanding. With the visual representation of data, it becomes easier for the non-technical members to understand how they can use the data insights to optimize business operations and stay a step ahead of their rival companies. Sense of Data Intuition Apart from being an extremely handy day-to-day tool for Data Scientists, Data Intuition is also a crucial part of job interviews. During interviews, employers will put all your abilities to test, including your intuitive ability to understand concepts related to Data Science. This is what we call ‘Data Intuition.” While it is true that you need to have strong mathematical, statistical, and visualization skills, you also should be able to determine what methods and techniques to use to solve a specific problem, what tools to use, and so on. Now that you know what skills you need to acquire to become a Data Scientist let’s look at the steps that will get you there! Data Scientists: Myths vs. Realities How to be a Data Scientist – The learning path The path to becoming a  Data Scientist is pretty straightforward. It starts from the start. Let’s walk you through it! Beginning it all. The first step involves understanding what Data Science is all about. Apart from learning all the basic concepts of Data Science, this is the stage where you make a choice of your first programming language and perfect it. The first few months will involve coding in the language of your choice. Once you are adept at coding in a particular language, learning other programming languages will become way more comfortable. Learning the basics of Mathematics and Statistics. Mathematics and Statistics make up the foundation for ML algorithms. Naturally, you’ll have to learn the basic concepts of Maths and Stats such as Mean, Median, Mode, Variance, Conditional Probability, Hypothesis Testing, Linear Algebra, Calculus, Descriptive Statistics, and Inferential Statistics, among other things. Learning ML concepts and their applications After mastering Maths and Stats concepts, it is time to move on to a more advanced area – Machine Learning. ML algorithms have found application in numerous real-world scenarios – from fraud detection and recommendation engines to sentiment analysis of customer feedback. Apart from the concepts mentioned before, you’ll also have to learn about Deep Learning, Artificial Neural Networks, Inductive Learning, etc. Gradually, as you get a hold of these ML concepts, you’ll have to experiment with them in real-world models through various validation strategies. Introduction to Deep Learning A subset of ML, Deep Learning, deals in algorithms that draw inspiration from the structure and function of brain-like artificial neural networks. These artificial neural nets imitate the functioning of the human brain. Deep learning models have at least three layers in which each layer receives information from the previous layer and passes it on to the next one. You must fully understand the functioning of Deep Learning, and to understand it, you’ll have to be well-versed in Linear and Logistics Regression. Deep Learning Architectures After getting the hang of Deep Learning, you must dive in to learn about advanced Deep Learning architectures like AlexNet, GoogleNet, recurrent neural networks (RNN) convolutional neural networks (CNN), region-based CNN (RCNN), SegNet, generative adversarial network (GAN), etc. Since these are quite hefty concepts, you need to dedicate a few weeks solely in understanding their functioning. Computer Vision Computer Vision (CV) is a scientific domain of study that seeks to find ways and develop techniques that will allow computers to understand digital content like videos and photographs. It involves “acquiring, processing, analyzing and understanding digital images” to attain highly specialized data from the real world to create numerical/symbolic information further. Being one of the hottest areas of exploration now, every aspiring Data Scientists needs to have a good knowledge of Computer Vision. NLP Natural Language Processing is an integral component of Data Science. Thus, every Data Scientist must have a strong understanding of NLP and its techniques. Primarily, NLP seeks to process, analyze, and understand natural language-based data (text, speech, etc.) through a combination of sophisticated tools and algorithms. While dealing with NLP, you’ll be learning about Data Retrieval (along with Web Scraping), Text Wrangling, Named Entity Recognition, Parts of Speech Tagging, Shallow Parsing, Constituency and Dependency Parsing, and Emotion and Sentiment Analysis. Concluding Thoughts Every day, the global data continues to increase, and with it is expanding the scope for innovation and creation. As Big Data and Data Science technologies continue to advance, the job portfolio of Data Scientists will also change in keeping with the times. So, how then, do you keep up? By upskilling. Data Science is a dynamic field that’s still evolving. To becomes a Data Scientist, you must always harbor an unquenchable thirst for knowledge and learning. If you do so, there’ll be nothing to stop you from shining in the field of Data Science.

Mankind has reached its peak of evolution and discovery. The consumer today looks for luxury and sophistication in the product and how it could benefit him or her in their daily life. To sustain and stay at the top of the market and give absolute comfort to the consumers, business organisations are using different strategies and technologies. Natural Language Processing or NLP is one such technology penetrating deeply and widely in the market, irrespective of the industry and domains. It is extensively applied in businesses today and it is the buzzword in every engineer’s life. In short, NLP is everywhere. 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 So what is NLP? In simple words, NLP or Natural Language Processing, also known as computational linguistics, is a blend of language, machine learning & artificial intelligence (AI). It builds a technology which allows us to interact with machines as in normal human to human conversation. ‘Hey Siri’ on your iPhone or ‘Ok Google’ on your Android mobile are the products of Natural Language Processing. A few years ago, we used to type keywords into Google search to get effective results. Today, you have the comfort of vocally seeking your help with this technology assistant. One of the most pragmatic tech trends, Natural Language Processing, has multiple applications in business today. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Some of the most important applications of Natural Language Processing for businesses in 2019: #1. Sentiment Analysis Mostly used on the web & social media monitoring, Natural Language Processing is a great tool to comprehend and analyse the responses to the business messages published on social media platforms. It helps to analyse the attitude and emotional state of the writer (person commenting/engaging with posts). This application is also known as opinion mining. It is implemented through a combination of Natural Language Processing and statistics by assigning values to the text (positive, negative or neutral) and in turn making efforts to identify the underlying mood of the context (happy, sad, angry, annoyed, etc.) This application of NLP helps business organisations gain insights on consumers and do a competitive comparison and make necessary adjustments in business strategies, whenever required. Such data is also useful in designing a better customer experience and enhancing the product. Furthermore, sentiment analysis or emotion exploration is a great way to know about brand perception. Sentiment Analysis: What is it and Why Does it Matter? #2. Chatbots We hear a lot about Chatbots these days, chatbots are the solution for consumer frustration regarding customer care call assistance. They provide modern-day virtual assistance for simple problems of the customer and offload low-priority, high turnover tasks which require no skill. Intelligent Chatbots are going to offer personalised assistance to the customer in the near future. A lot of Industry analysts predict that Chatbots are an emergent trend which will offer real-time solutions for simple customer service problems. They are unquestionably gaining a lot of trust and popularity from the consumer as well as engineers. They are useful in providing standard solutions to common problems. Chatbots help save time, human efforts, cost and provide efficient solutions (and keep improving from learning) from time to time. The Advent of Chatbots is Creating a Stir in Social Media #3. Customer Service Ensuring customer loyalty by keeping them content and happy is the supreme challenge and responsibility of every business organisation. NLP has aided in multiple functions of customer service and served as an excellent tool to gain insight into audience tastes, preferences and perceptions. Speech separation where the AI will identify each voice to the corresponding speaker and answer each of the callers separately. An excellent text to speech systems could even aid the blind. For example, a call recording of the customer can give insight into whether the customer is happy or sad, what are their needs and future requirements. NLP could aid in translating the caller’s speech into a text message which could be easily analysed by the engineer. To sum up, this would be a great way to get to know the pulse of your audience. Winning the Market with Consumer Journeys #4. Managing the Advertisement Funnel  What does your consumer need? Where is your consumer looking for his or her needs? Natural Language Processing is a great source for intelligent targeting and placement of advertisements in the right place at the right time and for the right audience. Reaching out to the right patron of your product is the ultimate goal for any business. NLP matches the right keywords in the text and helps to hit the right customers. Keyword matching is the simple task of NLP yet highly remunerative for businesses. The Complete Guide on How to Build Successful Sales Funnels Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. #5. Market Intelligence Business markets are influenced and impacted by market knowledge and information exchange between various organisations, stakeholders, governments and regulatory bodies. It is vital to stay up to date with industry trends and changing standards. NLP is a useful technology to track and monitor the market intelligence reports for and extract the necessary information for businesses to build new strategies. Widely used in financial marketing, NLP gives exhaustive insights into employment changes and status of the market, tender delays, and closings, or extracting information from large repositories. Exploratory Data Analysis and its Importance to Your Business These are some of the few applications of Natural Language Processing which will be witnessed by business organisations in the time to come. There are other applications as well, such as reputation monitoring, neural machine translation, hiring tools and management, regulatory compliance, data visualisation, biometrics, robotics, process automation etc. NLP is the key to the quest for general artificial intelligence since language is a key indicator of intelligence in our society. The Prospect The system behind the NLP concept is statistical in nature. For this concept to move from Natural Language Processing (NLP) to Natural Language Understanding (NLU) where the consumer can get to see and experience a human emotional connect with the machines, is the future prospect to work towards. Over the last decade, the information technology industry has taken its leap of faith and dug deep into the various aspects of the Natural Language Processing. Business organisations have found, tested and executed most favorable applications of NLP to advance the progress of Business Intelligence. Yet, the technology needs lots of data and processes in place to understand, analyse and respond to the needs of the human mind. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? 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Artificial Intelligence is rapidly taking on the world. It is helping scientists design such marvels that were once the subject of science fiction. From smart homes powered by IoT and intelligent personal assistants like Siri and Alexa to autonomous cars, AI is paving the way for the new age Digital Revolution. 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 Let’s look at instances when Artificial Intelligence conquered the impossible: 1. Emerged As A Winner In The Games Of Chess And Go The first win for Artificial Intelligence in the game sphere happened in 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov in two out of six games. This was not magic. IBM had fed Deep Blue’s system with a massive amount of data of games that took place in the past. Leveraging this data, Deep Blue learned to use a combination of brute force processing to win the game. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Then again in 2017, Google’s AlphaGo defeated Ke Jie, the world Go champion. This was an incredible feat as Go is a much more intuitive and mathematically complex game than chess. AlphaGo was so good at Go that it shocked Jie who stated: “I misjudged the capabilities of AlphaGo and felt powerless”. Go and the Challenge to Artificial General Intelligence Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. 2. Mastered Language One of the most significant achievements of AI has been in the field of Natural Language Processing (NLP). Leveraging the art of NLP, scientists have been able to create smart personal assistants such as Siri, Alexa, and Cortana. These intelligent assistants can not only understand human languages, but they can also engage in meaningful conversations with humans, shop for us, automate specific tasks, and so much more. 3. Nearly Won A Literary Award For Writing A Novel A novel titled “The Day A Computer Writes A Novel” passed the first round of screening test for Japan’s Hoshi Shinichi Literary Award. The judges only found out later that an AI had co-authored the novel. It is so impressive and well-crafted that it is now regarded as a literary piece in Japan. Satoshi Hase, a science fiction author and a judge of the Hoshi Shinichi Literary Award was deeply impressed by the abilities of the AI: “I was surprised at the work since it was a well-structured novel.” That’s not all, Google’s AI has further expanded its literary career by writing poems. 4. Deep Dream Google Brain researchers have discovered a unique way to visually demonstrate the thinking process of GoogleNet. Leveraging Generative Adversarial Networks (GANs), the researchers put forth a few images before Google Deep Dream. The result was astounding and almost ‘psychedelic.’ When images are presented before Deep Dream, it starts analyzing those images under the banner of every object that it was taught to recognize. In the process, it might find the resemblance of an object that is entirely unrelated to the image, and it then amplifies the image according to the similarities it found. And the results are otherworldly every time! These 6 Machine Learning Techniques are Improving Healthcare 5. Discovered An Eight-Planet Solar System AI’s forte is gathering massive amounts of data and using this data to accomplish unimaginable tasks. When astronomers at NASA applied AI on the data that had been collected by the Kepler telescope over many years, they discovered an eight-planet solar system located 2,500 light years away from us. The solar system was named Kepler-90i after the star it orbits – Kepler 90. This was accomplished with the help of a neural network that has been specially trained to identify exoplanets. 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 6. Chatbots That Can Communicate In A Mysterious Language Facebook had been working on building chatbots that could learn how to ‘negotiate.’ The chatbots named Alice and Bob had been designed to engage in trade negotiations with each other and much to everyone’s astonishment, they began conversing in their own secret language. This language was incomprehensible even to the researchers who built the chatbots. Eventually, Facebook had to shut down the chatbot project because of the serious implications it could have. This experiment proved that Artificial Intelligence holds the potential to drastically learn human behaviour and converse with one another by creating their own distinct language. Read: How to make chatbot in Python? Even though Artificial Intelligence is still a developing field, it has started impacting our lives in a major way. The most impressive trait of Artificial Intelligence has to be its ability to ‘learn’ new things and implement it in innovative ways to deliver results that continue to surprise the humankind. 

The air in the industry is electric with talks about Big Data. The power wielded by big data applications is spoken of with the same sense of awe as our ancestors would speak of the oracles of Greece – magical beings that predict the future; or of the djinns of central Asia, powerful constructs at your disposal, granting wishes. Even among those with a technical background, the smaller details of big data are murky to most. Technical concepts like HDFS, MapReduce are inherently quite difficult to grasp, even when you work with them. The bigger idea behind big data, however, seems to be clear to most – that there exists a large ocean of information, upon which we conduct some form of analysis, which is then used to draw insights on human behaviour. This idea of “more data equals more inference” is a well-understood one. Today, big data is a specific technical term, implying the use of certain technology. As the use of big data becomes more prevalent in all sectors of industry, we’re bound to see more references to it in the popular culture. However, in its basic form, we’ve known big data in our culture for a while. Big Data: What is it and Why does it Matter? What do people think about Big Data right now? Big data applications are all around us, and people are mostly sold on the utility value of big data for their business affairs. But there’s a flipside. Say the words big data to a person not exposed to the technical details of it, and it’s quite likely that the conversation will drift into visions of a dystopian future, people controlled and shackled by the machinations of corporations and governments, and eventually killer robots. People claim they feel like data points, all their information at the disposal of a few corporations, free to be used to bend your will and fill up their coffers. Even though all of us use some big data framework or the other, there’s a divide in the perception of the use cases of big data. We often hear scientists declaring that popular culture has not been very kind to big data. This has a grain of truth to it – all forms of popular culture, be it movies, TV shows or books, paint a rather grim picture of the future of the human race in a big data world, and for good reason! Writers of fiction, often times, take up the responsibility of playing out otherwise unimagined scenarios, to warn humans of their activities. The most telling signs of the future are very often found in good art. How Big Data and Machine Learning are Uniting Against Cancer Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Works of dystopia In any conversation surrounding modern works that describe the pitfalls of big data, the first instance that comes to most minds is George Orwell’s Nineteen Eighty-Four. Through the idea of Big Brother, this book gave us a sort of template for the ever-watching eye, tightly controlling people’s actions, thoughts and emotions. 1984 still remains the most common analogy people draw when they’re trying to express their fear of big data. The next most popular one is Terminator, where a military-industrial AI is fed data from every aspect of the US military. This AI attains sentience and immediately decides that humanity must die, giving rise to generations worth of fear. In fact, the community that works around regulation and policy involving big data and AI is well-aware of a curse they fondly refer to as the terminator syndrome. How can you possibly have a meaningful conversation about big data, when all conversations inevitably devolve into a terminator reference? Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Science fiction went through a golden age in the 50s and 60s, when the best selling books were all full of optimism for a scientific future. However, American writer Philip K Dick explored the dark side of scientific progress extensively. His stories are exemplary at highlighting nuanced effects of technological development. In the Spielberg adaptation of the Philip K Dick short story, Minority Report, three “pre-cogs” mimic big data analysis to predict crime before it occurs, with the hope of ending all crime. But one disparate prediction from one of the pre-cogs – a “minority prediction” –  starts to disrupt this system. The film is a great commentary on how systems meant for generalised predictions inevitably hurt minorities. It is the onus of humanity at large to ensure that the benefits of big data reach out to all people everywhere. They Say Data is the New Oil, Is it Really True? In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Are there any contemporary instances of this dystopia? The omniscient Machine — and it’s later iteration, Samaritan — perform big data analyses in the TV show Person of Interest, ostensibly to weed out terrorists and nip their plans in the bud, but practically keeping an eye on the entire population of the United States. This list is not complete without the most popular recent example of this genre, the stunning Netflix show Black Mirror. Many of these episodes tell stories about the need for human validation and demonstrates how easy access to a lot of validation driven by large quantities of data leads to a change in the whole fabric of society. Through all of these manifestations of big data that put humanity in danger, creative minds address the role of regulation, oversight and democratised access to big data. Optimism about a big data future Fortunately, a doomsday scenario is not the only end goal of fiction related to big data. Issac Asimov, a cornerstone of the mid-20th century Golden Age of science fiction, wrote the iconic ‘Foundation’ series. The premise of the series is psychohistory, a field that tries to predict human behaviour based on details of their history. In the book, the inventor of psychohistory, Hari Seldon, conceives some theorems that determine when psychohistory can be effective: The population under scrutiny is oblivious to the existence of the science of Psychohistory. The time periods dealt with are in the region of 3 generations. The population must be in the billions for a statistical probability to have a psychohistorical validity. One glance at these theorems and you can see how Asimov’s predictions, written in the 50s, have turned into reality today. Prediction of human behaviour on gigantic scales is part of the core business of companies like Google and Facebook. Big Data Roles and Salaries in the Finance Industry Why does art focus on dystopian possibilities of big data? In a nutshell, dystopia is exciting. Everybody loves a good disaster. There is no doubt that big data has made it easier to do business, find insights, build communities. But these phenomena will never be as catchy as a tragedy. Even though everyone understands that more information leads to more insights, the masses are more interested in the pitfall of this idea. In Nolan’s iconic film The Dark Knight, Batman creates a system where he turns every phone in the city into a listening device. In the end, it is revealed that he allows his trusty techie lieutenant Lucius Fox to destroy the whole system. With this move, Batman re-establishes himself as a likeable character – nobody wants to see that degree of control with one person. It’s not that people don’t inherently trust technology; they just don’t trust the people making and controlling technology. Technology enables more inference from more information, and what you infer depends wholly on the actions of the wielder of the technology. Facebook is a great example. Although most of us wouldn’t refuse a job at Facebook, many believe that the company records our voice as data input for their ads. The Facebook situation makes it amply clear that there is a deep distrust of the methods of data collection. You can see a similar type of reaction to the Aadhar database. Amidst major concerns towards security, a distrust grows towards the infrastructure surrounding the unique ID project. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? World politics are at a stage where fear has been weaponised globally, yet again. These visions of a bleak future stemming from big data, only add to this global cult of fear. Big Data Applications That Surround You What can we do? Regardless of the concerns voiced from across the world about big data, one thing is certain – we already live in the Age of Big Data. We cannot escape it; it is already all around us. Organisations utilise it in every aspect of the business. Almost all sectors of industry have found some application of big data. In the purview of this reality, there is no place in the world for fear driven by ignorance. Humans have always feared the unknown. Popular culture, while imitating life as good art does, reflects this fear by creating tangible bad actors. The antidote for this fear is the same one that has helped us through fears through the ages – education. Learning about big data will empower you to know more about the mechanisms of regulation and accountability, checks and balances about big data. Conclusion If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

Most of the discussions on Data Analysis deal with the “science” aspect of it. Surely, there’s a lot of science behind the whole process – the algorithms, formulas, and calculations, but you can’t take the “art” away from it. Structuring the complete process – from planning the analysis, to making sense of the final result – is no mean feat, and is no less than an art form. That is exactly what comes under our topic for the day – Exploratory Data Analysis. In this article, we’ll be looking at what is exploratory data analysis, what are the common tools and techniques for it, and how does it help an organisation. What is Exploratory Data Analysis? Exploratory Data Analysis is one of the important steps in the data analysis process. Here, the focus is on making sense of the data in hand – things like formulating the correct questions to ask to your dataset, how to manipulate the data sources to get the required answers, and others. This is done by taking an elaborate look at trends, patterns, and outliers using a visual method. Exploratory Data Analysis is a crucial step before you jump to machine learning or modeling of your data. It provides the context needed to develop an appropriate model – and interpret the results correctly. Data Manipulation: How Can You Spot Data Lies? Over the years, machine learning has been on the rise – and that’s given birth to a number of powerful machine learning algorithms. So powerful that they almost tempt you to skip the Exploratory Data Analysis phase. While it’s understandable why you’d want to take advantage of such algorithms and skip the EDA – It is not a very good idea to just feed data into a black box and wait for the results. It has been observed time and time again that Exploratory Data Analysis provides a lot of critical information which is very easy to miss – information that helps the analysis in the long run, from framing questions to displaying results. If you are a beginner and interested to learn more about data science, check out our data science training from top universities. While the aspects of EDA have existed as long as we’ve had data to analyse, Exploratory Data Analysis officially was developed back in the 1970s by John Turkey – the same scientist who coined the word “Bit” (short for Binary Digit). EDA is often seen and described as a philosophy more than science because there are no hard-and-fast rules for approaching it. The purpose of Exploratory Data Analysis is essential to tackle specific tasks such as: Spotting missing and erroneous data;   Mapping and understanding the underlying structure of your data;   Identifying the most important variables in your dataset;   Testing a hypothesis or checking assumptions related to a specific model;   Establishing a parsimonious model (one that can explain your data using minimum variables);   Estimating parameters and figuring the margins of error. Tools and Techniques used in Exploratory Data Analysis S-Plus and R are the most important statistical programming languages used to perform Exploratory Data Analysis. These languages come bundled with a plethora of tools that help you perform specific statistical functions like: Classification and dimension reduction techniques Classification is essentially used to group together different datasets based on a common parameter/variable. The data we’re talking about is multi-dimensional, and it’s not easy to perform classification or clustering on a multi-dimensional dataset. Hence, to help with that, Dimensionality Reduction techniques like PCA and LDA are performed – these reduce the dimensionality of the dataset without losing out on any valuable information from your data. How Does Simpson’s Paradox Affect Data? Univariate visualisation Univariate visualisations are essentially probability distributions of each and every field in the raw dataset – with summary statistics. Univariate visualisations use frequency distribution tables, bar charts, histograms, or pie charts for the graphical representation. Bivariate visualisations These allow the data scientists to assess the relationship between variables in your dataset – and helps you target the variable you’re looking at. Appropriate graphs for Bivariate Analysis depend on the type of variable in question. For instance, if you’re dealing with two continuous variables, a scatter plot should be the graph of your choice. If one is categorical and the other is continuous, a box plot is preferred and when both the variables are categorical, a mosaic plot is chosen. The Business of Data Security is Booming! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Multivariate visualisations Multivariate visualizations help in understanding the interactions between different data-fields. It involves observation and analysis of more than one statistical outcome variable at any given time. K-means clustering K-means clustering is basically used to create “centers” for each cluster based on the nearest mean. It’s an iterative technique that keeps creating and re-creating clusters – until the clusters formed stop changing with iterations. It can be used for finding outliers in a dataset (points that won’t be a form of any clusters will ideally be outliers). Predictive models As the name suggests, predictive modeling is a method that uses statistics to predict outcomes. Although most predictions aim to predict what’ll happen in the future, predictive modeling can also be applied to any unknown event, regardless of when it’s likely to occur. For example, this technique can be used to detect crime and identify suspects even after the crime has happened. The most common way of performing predictive modeling is using linear regression (see the image). The What’s What of Data Warehousing and Data Mining Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis How does Exploratory Data Analysis help your business and where does it fit in? Exploratory Data Analysis provides utmost value to any business by helping scientists understand if the results they’ve produced are correctly interpreted and if they apply to the required business contexts. Other than just ensuring technically sound results, Exploratory Data Analysis also benefits stakeholders by confirming if the questions they’re asking are right or not. Exploratory Data Science often turns up with unpredictable insights – ones that the stakeholders or data scientists wouldn’t even care to investigate in general, but which can still prove to be highly informative about the business. There are a number of data connectors that help organisations incorporate Exploratory Data Analysis directly into their Business Intelligence software. You can also set this up to allow data to flow the other way too, by building and running statistical models in (for example) R that use BI data and automatically update as new information flows into the model. Potential use-cases of Exploratory Data Analysis are wide-ranging, but ultimately, it all boils down to this – Exploratory Data Analysis is all about getting to know and understand your data before making any assumptions about it, or taking any steps in the direction of Data Mining. It helps you avoid creating inaccurate models or building accurate models on the wrong data. Performing this step right will give any organisation the necessary confidence in their data – which will eventually allow them to start deploying powerful machine learning algorithms. However, ignoring this crucial step can lead you to build your Business Intelligence System on a very shaky foundation. 12 Ways to Connect Data Analytics to Business Outcomes upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4     In Conclusion… Exploratory Data Analysis is quite clearly one of the important steps during the whole process of knowledge extraction. If you want to set up a strong foundation for your overall analysis process, you should focus with all your strength and might on the EDA phase. In all honesty, a bit of statistics is required to ace this step. If you feel you lag behind on that front, don’t forget to read our article on Basics of Statistics Needed for Data Science. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science. Oh, and what do you feel about our stand of considering “Exploratory Data Analysis” as an art more than science? Let us know in the comments below!