Learn Data Science for 100% free from NPTEL Online Courses

NPTEL is an excellent initiative from the Government of India to provide free online courses taught by faculties from IISc, IITs, and other top Indian institutes. NPTEL platform offers courses related to various branches of Engineering like Aerospace Engineering, Biotechnology, Chemical Engineering, Civil Engineering, Computer Science, etc. Anyone can access these online courses without paying any money. These courses are top-notch as they are taught by faculty with years of research and teaching experience at highly reputed Indian institutes like IISc, IITs, and others. Now let us see the various Data Science courses offered by NPTEL platform.

♟Python Programming

Learning Python programming is the first step in the journey of any Data Science enthusiast. With simple and easy-to-follow syntax, you can quickly learn python programming even if you are entirely new to programming. If you are already familiar with programming in other languages, you can learn python programming much easier.

🎯 The Joy of Computing using Python

This is an excellent NPTEL course for Python programming taught by Prof. Sudarshan Iyengar from IIT Ropar.

Here is the list of topics covered by this course

• Welcome to Programming!!

• Variables and Expressions : Design your own calculator

• Loops and Conditionals : Hopscotch once again

• Lists, Tuples and Conditionals : Lets go on a trip

• Abstraction Everywhere : Apps in your phone

• Counting Candies : Crowd to the rescue

• Birthday Paradox : Find your twin

• Google Translate : Speak in any Language

• Currency Converter : Count your foreign trip expenses

• Monte Hall : 3 doors and a twist

• Sorting : Arrange the books

• Searching : Find in seconds

• Substitution Cipher : What’s the secret !!

• Sentiment Analysis : Analyse your Facebook data

• 20 questions game : I can read your mind

• Permutations : Jumbled Words

• Spot the similarities : Dobble game

• Count the words : Hundreds, Thousands or Millions.

• Rock, Paper and Scissor : Cheating not allowed !!

• Lie detector : No lies, only TRUTH

• Calculation of the Area : Don’t measure.

• Six degrees of separation : Meet your favourites

• Image Processing : Fun with images

• Tic tac toe : Let’s play

• Snakes and Ladders : Down the memory lane.

• Recursion : Tower of Hanoi

• Page Rank : How Google Works !!

🎈 Course link: The Joy of Computing using Python

🎯 Python for Data Science

This is an excellent NPTEL course for Python programming taught by Prof. Ragunathan Rengasamy from IIT Madras.

Here are the course contents

• Week 1: BASICS OF PYTHON (Variables, Operators, Data Types)

• Week 2: Sequence data types and associated operations

• Week 3: Control Structures and Functions

• Week 4: CASE STUDY (Regression and Classification)

🎈Course link: Python for Data Science

♟Data Analytics

🎯 Data Analytics with Python

This is an excellent NPTEL course for Data Analytics taught by Prof. A Ramesh from IIT Roorkee.

This course covers the following topics

Week 1 : Introduction to data analytics and Python fundamentals

Week 2 : Introduction to probability

Week 3 : Sampling and sampling distributions

Week 4 : Hypothesis testing

Week 5 : Two sample testing and introduction to ANOVA

Week 6 : Two way ANOVA and linear regression

Week 7 : Linear regression and multiple regression

Week 8 : Concepts of MLE and Logistic regression

Week 9 : ROC and Regression Analysis Model Building

Week 10 : c² Test and introduction to cluster analysis

Week 11 : Clustering analysis

Week 12 : Classification and Regression Trees (CART)

🎈Course Link: Data Analytics with Python

♟Data Science

🎯 Data Science for Beginners

This is an excellent NPTEL course taught by Prof. Shankar Narasimhan, and Prof. Ragunathan Rengasamy from IIT Madras.

Here are the course contents

• Course philosophy and introduction to R

• Linear algebra for data science

• Statistics for data science

• Optimization

• Regression

• Classification using logistic regression

• Classification using kNN and k-means clustering

🎈Course link: Data Science for Beginners

🎯 Scalable Data Science

This is an excellent NPTEL course taught by Prof. Anirban Dasgupta, Prof. Sourangshu Bhattacharya from IIT Kharagpur.

Here are the course contents

• Week 1 : Background: Introduction (30 mins) Probability: Concentration inequalities, (30 mins) Linear algebra: PCA, SVD (30 mins) Optimization: Basics, Convex, GD. (30 mins) Machine Learning: Supervised, generalization, feature learning, clustering.

• Week 2: Memory-efficient data structures: Hash functions, universal / perfect hash families (30 min) Bloom filters (30 mins) Sketches for distinct count (1 hr) Misra-Gries sketch. (30 min)

• Week 3: Memory-efficient data structures (contd.): Count Sketch, Count-Min Sketch (1 hr) Approximate near neighbors search: Introduction, kd-trees etc (30 mins) LSH families, MinHash for Jaccard, SimHash for L2 (1 hr)

• Week 4: Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (1.5 hr) Randomized Numerical Linear Algebra Random projection (1 hr)

• Week 5 : Randomized Numerical Linear Algebra CUR Decomposition (1 hr) Sparse RP, Subspace RP, Kitchen Sink (1.5 hr)

• Week 6 : Map-reduce and related paradigms Map reduce - Programming examples - (page rank, k-means, matrix multiplication) (1 hr) Big data: computation goes to data. + Hadoop ecosystem (1.5 hrs)

• Week 7 : Map-reduce and related paradigms (Contd.) Scala + Spark (1 hr) Distributed Machine Learning and Optimization: Introduction (30 mins) SGD + Proof (1 hr)

• Week 8 : Distributed Machine Learning and Optimization: ADMM + applications (1 hr) Clustering (1 hr) Conclusion (30 mins)

🎈Course link: Scalable Data Science

♟Machine Learning

🎯 Introduction to Machine Learning

This is an excellent ML course taught by Dr. Balaraman Ravindran from IIT Madras.

Here are the course contents

Week 0: Probability Theory, Linear Algebra, Convex Optimization - (Recap)
Week 1: Introduction: Statistical Decision Theory - Regression, Classification, Bias Variance
Week 2: Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
Week 3: Linear Classification, Logistic Regression, Linear Discriminant Analysis
Week 4: Perceptron, Support Vector Machines
Week 5: Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation
Week 6: Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation Measures
Week 7: Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting
Week 8: Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks
Week 9: Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
Week 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering
Week 11: Gaussian Mixture Models, Expectation Maximization
Week 12: Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

🎈Course link: Introduction to Machine Learning

🎯 Machine Learning

This is an excellent ML introduction course taught by Prof. Carl Gustaf Jansson from KTH, The Royal Institute of Technology.

Week 1 : Introduction to the Machine Learning course
Week 2 : Characterization of Learning Problems
Week 3 : Forms of Representation
Week 4 : Inductive Learning based on Symbolic Representations and Weak Theories
Week 5 : Learning enabled by Prior Theories
Week 6 : Machine Learning based Artificial Neural Networks
Week 7 : Tools and Resources + Cognitive Science influences
Week 8 : Examples, demos and exam preparations

🎈Course link: Machine Learning

🎯 Optimisation for Machine Learning: Theory and Implementation

This is an excellent NPTEL course taught by Prof. Pravesh Biyani from IIIT Delhi.

🎈Course link: Optimisation for Machine Learning: Theory and Implementation

🎯 Introduction to Machine Learning

This is an excellent ML course taught by Prof. S. Sarkar from IIT Kharagpur

Week 1: Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
Week 2: Linear regression, Decision trees, overfitting
Week 3: Instance based learning, Feature reduction, Collaborative filtering based recommendation
Week 4: Probability and Bayes learning
Week 5: Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
Week 6: Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
Week 7: Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
Week 8: Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model

🎈Course link: Introduction to Machine Learning

🎯 Machine Learning for Engineering and Science Applications

This is an excellent applied ML course taught by Prof. Balaji Srinivasan, Prof. Ganapathy from IIT Madras.

• Week 1 : Mathematical Basics 1 – Introduction to Machine Learning, Linear Algebra

• Week 2 : Mathematical Basics 2 -- Probability

• Week 3 : Computational Basics – Numerical computation and optimization, Introduction to Machine Learning packages

• Week 4 : Linear and Logistic Regression – Bias/Variance Tradeo, Regularization, Variants of Gradient Descent, MLE, MAP, Applications

• Week 5 : Neural Networks – Multilayer Perceptron, Backpropagation, Applications

• Week 6 : Convolutional Neural Networks 1 – CNN Operations, CNN architectures

• Week 7 : Convolutional Neural Networks 2 – Training, Transfer Learning, Applications

• Week 8 : Recurrent Neural Networks ¬– RNN, LSTM, GRU, Applications

• Week 9 : Classical Techniques 1 – Bayesian Regression, Binary Trees, Random Forests, SVM, Naïve Bayes, Applications

• Week 10 : Classical Techniques 2 – k-Means, kNN, GMM, Expectation Maximization, Applications

• Week 11 : Advanced Techniques 1 – Structured Probabilistic Models, Monte Carlo Methods

• Week 12 : Advanced Techniques 2 – Autoencoders, Generative Adversarial Networks

🎈 Course link: Machine Learning for Engineering and Science Applications

🎯Practical Machine Learning with Tensorflow

This is an excellent ML course taught by Prof. Ashish Tendulkar, and Dr. B. Ravindran from IIT Madras.

Week 1: Getting started with Tensorflow

Week 2: Overview of Machine Learning (Process and Techniques, Demonstration of ML concepts with Deep Playground)

Week 3: Data Input and Preprocessing with Tensorflow

Week 4: Machine Learning Model Building

Week 5: Prediction with Tensorflow

Week 6: Monitoring and evaluating models using Tensorboard

Week 7: Advance Tensorflow (Building custom models - CNNs, Scaling up for large datasets)

Week 8: Distributed training with hardware accelerators

🎈 Course link: Practical Machine Learning with Tensorflow

🎯 Machine Learning for Earth System Sciences

This is an excellent applied ML course taught by Prof. Adway Mitra from IIT Kharagpur.

Week 1: Recap of probability, spatio-temporal statistics (autoregression, geostatistical equation, Gaussian Processes, Extreme value statistics)

Week 2: Recap of relevant Machine Learning and Deep Learning techniques (Bayesian Networks, CNN, RNN/LSTM, VaE, Interpretability, Causality)

Week 3: Earth System Process Understanding: case studies (predictors of monsoon, extreme weather forecasting, climate change visualization)

Week 4: Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis)

Week 5: Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis)

Week 6: Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis)

Week 7: Earth System Modeling: relevant concepts (Model structures, modeling challenges, model validation, data assimilation)

Week 8: Earth System Modeling: applications in different domains (ML-based surrogate models, deep and shallow generators, long-term forecasting)

🎈 Course link: Machine Learning for Earth System Sciences

♟Deep Learning

🎯 Deep Learning- Part 1 (IIT Ropar)

This is an excellent deep learning course offered by Prof. Sudarshan Iyengar from IIT Ropar.

Here are the contents of this course

Week 1 : (Partial) History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm
Week 2 : Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks
Week 3 : FeedForward Neural Networks, Backpropagation
Week 4 : Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis
Week 5 : Principal Component Analysis and its interpretations, Singular Value Decomposition
Week 6 : Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders
Week 7 : Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout
Week 8 : Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization
Week 9 : Learning Vectorial Representations Of Words
Week 10: Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks
Week 11: Recurrent Neural Networks, Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs
Week 12: Encoder Decoder Models, Attention Mechanism, Attention over images

🎈Course link: Deep Learning

🎯 Deep Learning, IIT Kharagpur

This is an excellent deep learning course offered by Prof. P.K. Biswas from IIT Kharagpur.

Week 1: Introduction to Deep Learning, Bayesian Learning, Decision Surfaces

Week 2: Linear Classifiers, Linear Machines with Hinge Loss

Week 3: Optimization Techniques, Gradient Descent, Batch Optimization

Week 4: Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning

Week 5: Unsupervised Learning with Deep Network, Autoencoders

Week 6: Convolutional Neural Network, Building blocks of CNN, Transfer Learning

Week 7: Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

Week 8: Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization

Week 9: Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.

Week 10: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.

Week 11: LSTM Networks

Week 12: Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

🎈 Course link: Deep Learning (IIT Kharagpur)

🎯Deep Learning - Part 2 ( IIT Madras)

This is an advanced deep learning course offered by Prof. Mitesh Khapra from IIT Madras.

Module 1 : A brief introduction to Directed Graphical Models
Module 2 : A brief introduction to Markov Networks, Using joint distributions for classification and sampling, Latent variables
Module 3 : Restricted Boltzmann Machines, Unsupervised Learning, Motivation for Sampling, Markov Chains, Gibbs Sampling for training RBMs, Contrastive Divergence for training RBMs
Module 4 : Variational Autoencoders, Autoregressive models, GANs

🎈 Course link: Deep Learning Part-2

🎯Deep Learning for Computer Vision

This is an excellent deep learning course focused on computer vision offered by Prof. Vineeth N Balasubramanian, IIT Hyderabad.

Here are the course contents

Week 1:Introduction and Overview:

○ Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution

Week 2:Visual Features and Representations:

○ Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG, LBP, etc.

Week 3:Visual Matching:

○ Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow

Week 4:Deep Learning Review:

○ Review of Deep Learning, Multi-layer Perceptrons, Backpropagation

Week 5:Convolutional Neural Networks (CNNs):

○ Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG, InceptionNets, ResNets, DenseNets

Week 6:Visualization and Understanding CNNs:

○ Visualization of Kernels; Backprop-to-image/Deconvolution Methods; Deep Dream, Hallucination, Neural Style Transfer; CAM,Grad-CAM, Grad-CAM++; Recent Methods (IG, Segment-IG, SmoothGrad)

Week 7:CNNs for Recognition, Verification, Detection, Segmentation:

○ CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet; CNNs for Segmentation: FCN, SegNet, U-Net, Mask-RCNN

Week 8:Recurrent Neural Networks (RNNs):

○ Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition

Week 9:Attention Models:

○ Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks

Week 10:Deep Generative Models:

○ Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etc

Week 11:Variants and Applications of Generative Models in Vision:

○ Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security; Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etc

Week 12:Recent Trends:

○ Zero-shot, One-shot, Few-shot Learning; Self-supervised Learning; Reinforcement Learning in Vision; Other Recent Topics and Applications

🎈Course link: Deep Learning for Computer Vision

♟ Reinforcement Learning

This is an excellent reinforcement learning course offered by Dr. B. Ravindran from IIT Madras.

Here are the course contents

Week 1 Introduction

Week 2 Bandit algorithms – UCB, PAC

Week 3 Bandit algorithms –Median Elimination, Policy Gradient

Week 4 Full RL & MDPs

Week 5 Bellman Optimality

Week 6 Dynamic Programming & TD Methods

Week 7 Eligibility Traces

Week 8 Function Approximation

Week 9 Least Squares Methods

Week 10 Fitted Q, DQN & Policy Gradient for Full RL

Week 11 Hierarchical RL

Week 12 POMDPs

🎈 Course link: Reinforcement Learning