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Our Learners
"The Data Science course was handled very well by the IIT Madras Professors. They've put enormous effort to make us understand this highly technical course and answered the doubts of all the students. I recommend this course."
Vasanth
"I took the Data Science program, which consisted of multiple classes. Overall the teachers knew the subject and covered what was promised.I would recommend this course to everyone."
Udhay
"The course design is excellent especially for beginners to study and understand the basic concepts in Data Science. The lessons and course material are perfect and apt for this courselevel."
Vaseem
Course Syllabus
Introduction
16 Lesson
3 hrs
 Introduction
 What is Data Science?
 Collecting Data
 Storing Data
 Processing Data
 Describing Data
 Statistical Modelling
 Algorithmic Modelling
 Why is Data Science so popular today
 Are AI and Data Science related?
 Problem Solving
 Knowledge Representation & Reasoning
 Decision Making
 Communication, Perception & Actuation
 The Myths of Data Science
 The Path to Data Science
 Feedback: Introduction
 Week1 Quiz Test (Graded & compulsory)
 Week1 Quiz Explained (optional)
Engineering Data Science System
9 Lesson
2 hrs
 Engineering Aspects of Data Science
 System Perspective of Data Science
 CRISP  DM_Business Understanding
 CRISP  DM_Data Understanding, Preparation & Modelling
 CRISP  DM_Evaluation & Deployment
 Programming Tools
 Why Python?
 Python  Libraries
 Summary
 Feedback: Engineering Data Science Systems
 Week 2 Part1 Quiz Test ( Graded & Compulsory)
 Week 2 Part1 Quiz Explained (Optional)
What is Statistics?
11 Lesson
2 hrs
 Introduction to Statistics
 What is Statistics
 How to select a Sample
 How to Design an Experiment
 How to Describe & Summarise Data
 Why do we need Probability Theory?
 How do we give guarantees for estimates made from sample
 What is a hypothesis & How do we test it?
 How to model relationship between variables?
 How well does the model fit the data?
 Summary
 Feedback: What is Statistics
 Week2 Part2 Quiz Test (Graded & compulsory)
 Week2 Part2 Quiz Explained (Optional)
Getting started with Python
13 Lesson
3 hrs

li>Getting started with Python
 Google Colab
 Printing & Basic Data Types
 Variables
 Integers, Floating Points, Boolean types & Input
 Processing Strings, Integers & Floating Points
 If, For, While Blocks
 Functions
 Assignment Problems
 Solution to Assignment Problem 1  Part 1
 Solution to Assignment Problem 1  Part 2
 Solution to Assignment Problem 2  Part 1
 Solution to Assignment Problem 2  Part 2
Python (continued)
12 Lesson
3 hrs
 Basics of Pytorch: Outline
 Basics of Pytorch: PyTorch Tensors
 Basics of Pytorch: Simple Tensor Operations
 Basics of Pytorch: NumPy vs PyTorch
 Basics of Pytorch: GPU PyTorch
 Basics of Pytorch: Automatic Differentiation
 Basics of Pytorch: Loss Function with AutoDiff
 Basics of Pytorch: Learning Loop GPU
 FNNs using Pytorch: Outline
 FNNs using Pytorch: Forward Pass With Tensors
 FNNs using Pytorch: Functions for Loss, Accuracy, Backpropagation
 FNNs using Pytorch: PyTorch Modules  NN and Optim
 FNNs using Pytorch: NN Sequential and Code Structure
 FNNs using Pytorch: GPU Execution
 FNNs using Pytorch: Exercises and Recap
Descriptive Statistics (Part 1)
12 Lesson
3 hrs
 Introduction to Descriptive Statistics
 Different types of Data
 How to describe Qualitative Data?
 Course Insights
 How to describe Quantative Data? Histograms
 Histograms Continued...
 Typical Trends in Histograms
 Uses of Histograms in ML
 Stem and Leaf Plots
 How to describe relationship between variables? Scatter Plots
 Uses of Scatter Plots in ML
 Summary
Descriptive Statistics (Part 2)
10 Lesson
2 hrs
 The convolution operation: Setting the Context
 The convolution operation: The 1D convolution operation
 The convolution operation: The 2D Convolution Operation
 The convolution operation: Examples of 2D convolution
 The convolution operation: 2D convolution with a 3D filter
 The convolution operation: Terminilogy
 The convolution operation: Padding and Stride
 From convolution operation to neural networks: How is the convolution operation related to Neural Networks  Part 1
 From convolution operation to neural networks: How is the convolution operation related to Neural Networks  Part 2
 From convolution operation to neural networks: How is the convolution operation related to Neural Networks  Part 3
 From convolution operation to neural networks: Understanding the input/output dimensions
 From convolution operation to neural networks: Sparse Connectivity and Weight Sharing
 From convolution operation to neural networks: Max Pooling and NonLinearities
 From convolution operation to neural networks: Our First Convolutional Neural Network (CNN)
 From convolution operation to neural networks: Training CNNs
 From convolution operation to neural networks: Summary and what next
 CNNs in Pytorch: Outline
 CNNs in Pytorch: Loading Data Sets
 CNNs in Pytorch: Visualising Weights
 CNNs in Pytorch: Single Convolutional Layer
 CNNs in Pytorch: Deep CNNs
 CNNs in Pytorch: LeNet
 CNNs in Pytorch: Training Le Net
 CNNs in Pytorch: Visualising Intermediate Layers, Exercises
Descriptive Statistics (Part 3)
16 Lesson
3 hrs
 CNN Architectures  Part 1: Setting the context
 CNN Architectures  Part 1: The Imagenet Challenge
 CNN Architectures  Part 1: Understanding the first layer of AlexNet
 CNN Architectures  Part 1: Understanding all layers of AlexNet
 CNN Architectures  Part 1: ZFNet
 CNN Architectures  Part 1: VGGNet
 CNN Architectures  Part 1: Summary
 CNN Architectures  Part 2: Setting the context
 CNN Architectures  Part 2: Number of computations in a convolution layer
 CNN Architectures  Part 2: 1x1 Convolutions
 CNN Architectures  Part 2: The Intuition behind GoogLeNet
 CNN Architectures  Part 2: The Inception Module
 CNN Architectures  Part 2: The GoogleNet Architecture
 CNN Architectures  Part 2: Average Pooling
 CNN Architectures  Part 2: Auxiliary Loss for training a deep network
 CNN Architectures  Part 2: ResNet
 Building CNN Architectures Using Pytorch: Outline
 Building CNN Architectures Using Pytorch: Image Transforms
 Building CNN Architectures Using Pytorch: VGG
 Building CNN Architectures Using Pytorch: Training VGG
 Building CNN Architectures Using Pytorch: Pretrained Models
 Building CNN Architectures Using Pytorch: Checkpointing Models
 Building CNN Architectures Using Pytorch: ResNet
 Building CNN Architectures Using Pytorch: Inception Part 1
 Building CNN Architectures Using Pytorch: Inception Part 2
 Building CNN Architectures Using Pytorch: Exercises
 Visualising CNNs: Receptive field of a neuron
 Visualising CNNs: Identifying images which cause certain neurons to fire
 Visualising CNNs: Visualising filters
 Visualising CNNs: Occlusion experiments
 Visualising CNNs Using Python: Outline
 Visualising CNNs Using Python: Custom Torchvision Dataset
 Visualising CNNs Using Python: Visualising inputs
 Visualising CNNs Using Python: Occlusion
 Visualising CNNs Using Python: Visualising filters
 Visualising CNNs Using Python: Visualising filters  code
 Batch Normalization and Dropout: Normalizing inputs
 Batch Normalization and Dropout: Why should we normalize the inputs
 Batch Normalization and Dropout: Batch Normalization
 Batch Normalization and Dropout: Learning Mu and Sigma
 Batch Normalization and Dropout: Ensemble Methods
 Batch Normalization and Dropout: The idea of dropout
 Batch Normalization and Dropout: Training without dropout
 Batch Normalization and Dropout: How does weight sharing help ?
 Batch Normalization and Dropout: Using dropout at test time
 Batch Normalization and Dropout: How does dropout act as a regularizer ?
 Batch Normalization and Dropout: Summary and what next ?
 Batch Normalization and Dropout Using Python: Outline and Dataset
 Batch Normalization and Dropout Using Python: Batch Norm Layer
 Batch Normalization and Dropout Using Python: Batch Norm Visualisation
 Batch Normalization and Dropout Using Python: Batch Norm 2d
 Batch Normalization and Dropout Using Python: Dropout layer
 Batch Normalization and Dropout Using Python: Dropout Visualisation and Exercises
 Hyperparameter Tuning and MLFlow: Outline
 Hyperparameter Tuning and MLFlow: Colab on Local Runtime
 Hyperparameter Tuning and MLFlow: MLFlow installation and basic usage
 Hyperparameter Tuning and MLFlow: Hyperparamater Tuning
 Hyperparameter Tuning and MLFlow: Refined Search for Hyperparameters
 Hyperparameter Tuning and MLFlow: Logging Image Artifacts
 Hyperparameter Tuning and MLFlow: Logging and Loading Models
 Hyperparameter Tuning and MLFlow: One Last Visualisation
Numpy
13 Lesson
3 hrs
 Sequence Learning Problems: Setting the context
 Sequence Learning Problems: Introduction to sequence learning problems
 Sequence Learning Problems: Some more examples of sequence learning problems
 Sequence Learning Problems: Sequence learning problems using video and speech data
 Sequence Learning Problems: A wishlist for modelling sequence learning problems
 Sequence Learning Problems: Intuition behind RNNs  Part 1
 Sequence Learning Problems: Intuition behind RNNs  Part 2
 Sequence Learning Problems: Introducing RNNs
 Sequence Learning Problems: Summary and what next
 Recurrent Neural Networks: Setting the context
 Recurrent Neural Networks: Data and Tasks  Sequence Classification  Part 1
 Recurrent Neural Networks: Data and Tasks  Sequence Classification  Part 2
 Recurrent Neural Networks: A clarification about padding
 Recurrent Neural Networks: Data and Tasks  Sequence Labelling
 Recurrent Neural Networks: Model
 Recurrent Neural Networks: Loss Function
 Recurrent Neural Networks: Learning Algorithm
 Recurrent Neural Networks: Learning Algorithm  Derivatives w.r.t. V
 Recurrent Neural Networks: Learning Algorithm  Derivatives w.r.t. W
 Recurrent Neural Networks: Evaluation
 Recurrent Neural Networks: Summary and what next
 Vanishing and Exploding Gradients: Revisiting the gradient wrt W
 Vanishing and Exploding Gradients: Zooming into one element of the chain rule  Part 1
 Vanishing and Exploding Gradients: Zooming into one element of the chain rule  Part 2
 Vanishing and Exploding Gradients: A small detour to calculus
 Vanishing and Exploding Gradients: Looking at the magnitude of the derivative
 Vanishing and Exploding Gradients: Exploding and vanishing gradients
 Vanishing and Exploding Gradients: Summary and what next
 LSTMs and GRUs: Dealing with longer sequences
 LSTMs and GRUs: The white board analogy
 LSTMs and GRUs: Real world example of longer sequences
 LSTMs and GRUs: Going back to RNNs
 LSTMs and GRUs: Selective Write  Part 1
 LSTMs and GRUs: Selective Write  Part 2
 LSTMs and GRUs: Selective Read
 LSTMs and GRUs: Selective forget
 LSTMs and GRUs: An example computation with LSTMs
 LSTMs and GRUs: Gated recurrent units
 LSTMs and GRUs: Summary and what next
 Sequence Models in PyTorch: OutlineSequence Models in PyTorch: Outline
 Sequence Models in PyTorch: Dataset and Task
 Sequence Models in PyTorch: RNN Model
 Sequence Models in PyTorch: Inference on RNN
 Sequence Models in PyTorch: Training RNN
 Sequence Models in PyTorch: Training Setup
 Sequence Models in PyTorch: LSTM
 Sequence Models in PyTorch: GRU and Exercises
 Addressing the problem of vansihing and exploding gradients: Quick Recap
 Addressing the problem of vansihing and exploding gradients: Intuition: How gates help to solve the problem of vanishing gradients
 Addressing the problem of vansihing and exploding gradients: Revisiting vanishing gradients in RNNs
 Addressing the problem of vansihing and exploding gradients: Dependency diagram for LSTMs
 Addressing the problem of vansihing and exploding gradients: Computing the gradient
 Addressing the problem of vansihing and exploding gradients: When do the gradients vanish?
 Addressing the problem of vansihing and exploding gradients: Dealing with exploding gradients
 Addressing the problem of vansihing and exploding gradients: Summary and what next
 Batching for Sequence Models in Pytorch: Overview
 Batching for Sequence Models in Pytorch: Recap on Sequence Models
 Batching for Sequence Models in Pytorch: Batching for Sequence Models
 Batching for Sequence Models in Pytorch: Padding Vector Representations
 Batching for Sequence Models in Pytorch: Packing in PyTorch
 Batching for Sequence Models in Pytorch: Training with Batched Input
Pandas
8 Lesson
2 hrs
 Neural Encoders and Decoders: Setting the context
 Neural Encoders and Decoders: Revisiting the task of language modelling
 Neural Encoders and Decoders: Using RNNs for language modelling
 Neural Encoders and Decoders: Introducing Encoder Decoder Model
 Neural Encoders and Decoders: Connecting encoder decoder models to the six jars
 Neural Encoders and Decoders: A compact notation for RNNs, LSTMs and GRUs
 Neural Encoders and Decoders: Encoder decoder model for image captioning
 Neural Encoders and Decoders: Six jars for image captioning
 Neural Encoders and Decoders: Encoder decoder for Machine translation
 Neural Encoders and Decoders: Encoder decoder model for transliteration
 Neural Encoders and Decoders: Summary
 Attention Mechanism: Motivation for attention mechanism
 Attention Mechanism: Attention mechanism with an oracle
 Attention Mechanism: A model for attention
 Attention Mechanism: The attention function
 Attention Mechanism: Machine translation with attention
 Attention Mechanism: Summary and what next
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Outline
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Data set and Task
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Data Ingestion  XML processing
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Encoder Decoder Model  1
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Encoder Decoder Model  2
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Adding Attention  1
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Adding Attention  2
 Encoder Decoder Models and Attention Mechanism Using Pytorch: Model Evaluation and Exercises
Pandas (continued)
11 Lesson
2 hrs
 RCNN: More clarity on regression
 RCNN: Setting the context
 RCNN: A typical pipeline for object detection
 RCNN: Region Proposal
 RCNN: Feature Extraction
 RCNN: Classification
 RCNN: Regression
 RCNN: Training
 YOLO: Introduction to YOLO
 YOLO: The Output of YOLO
 YOLO: Training
 Object Detection: Summary and what next
Visualisation
14 Lesson
3 hrs
 Data Visualisation
 Read Complex JSON files
 Styling Tabulation
 Distribution of Data  Histogram
 Box Plot
 Distribution of a categorical variable
 Joint Distribution of two variables
 Swarm Plot
 Violin Plot
 Multiple Violin Plots
 Paired Violin Plot
 Faceted plotting
 Pair Plot
 Boxen Plots
Visualisation (Continued)
13 Lesson
3 hrs
 Data Visualization  Recap
 Pie Chart
 Donut Chart
 Stacked Bar Plot
 Relative Stacked Bar Plot
 Time  Varying compostion of data
 Stacked Area Plot
 Scatter Plots
 Bar Plot
 Continuous vs Continuous Plot
 Line Plot
 Line Plot Covid Data
 Heat Map
 Summary & Task on openended visualisation
Approaching Open ended DS problems
6 Lesson
2 hrs
 Pandas Recap
 Handling missing data
 Missing data with Pandas
 Open ended descriptive statistics
 Agriculture Example Part 1
 Agriculture Example Part 2
Counting
12 Lesson
12 hrs
 Why do we need Counting and Probability Theory?
 Very Simple Counting
 The Multiplication Principle
 Multiplication Principle Special Case: Sequences with Repetition
 Multiplication Principle Special Case: Sequences without Repetition
 Example: A Different Kind of Sequence
 Multiplication Principle Special Case: Sequence Length Equals the Number of Objects
 The Subraction Principle
 Collections
 Collections (Some Examples)
 Collections with Repetitions
 Collections (+ multiplication principle)
 Collections (+ subraction principle)
 Summary
Sample spaces & Events
19 Lesson
4 hrs
 Introduction
 The Element of Chance (Nothing in life is certain)
 A brief overview of Set Theory
 Properties of Set Operations
 Experiments & Sample spaces
 Events of an Experiment
 Axioms of Probability
 Some properties of Probability
 Example problems (Probability Theory)
 Designing Probablity functions (as relative frequency)
 Designing Probablity functions (equally likely outcomes)
 Summary  1
 Conditional Probabilities
 Examples (Conditional Probabilities)
 The Multiplication Principle
 Total Probability Theorem
 Bayes' Theorem
 Independent Events
 Summary  2
Random Variables
20 Lesson
4 hrs
 Introduction
 Random Variable
 Probability Mass Functions
 Properties of PMF
 Disctrete distributions
 Bernoulli Distribution
 Binomial Distribution
 Example (Binomial Distribution)
 More Examples (Binomial Distribution)
 Is Binomial Distribution a valid distribution ?
 Geometric Distribution
 Is Geometric distribution a valid distribution ?
 Uniform Distribution
 Expectation
 Examples  Expectation
 Properties of Expectation
 Function of a Random Variable
 Variance of a Random Variable
 Properties of Variance
 Summary
Distributions & Sampling Strategies
10 Lesson
2 hrs
 Introduction
 Continuous Random Variable
 Intution : Density vs Mass
 Uniform Distribution (Continuous)
 Some Fun with Functions
 Normal Distributions
 Probability Density Function
 Standard Normal Distribution
 Sampling Methods
 Experimental Studies
Distributions of Sample Statistics
17 Lesson
3 hrs
 Introduction  Inferential Statistics
 Distribution of Sample Statistics
 Parameter
 Sample
 Why do we Compute Statistics ?
 Estimate Population Parameters
 Random Sample
 Recap : Probability
 Probability Space
 What kind of random variables ?
 What is inferential statistics?
 Our Roadmap
 Demo 01
 Demo 02
 Demo Problems
 Exercise  Part 1
 Exercise  Part 2
Central Limit Theorem
13 Lesson
2 hrs
 Central Limit Theorem
 Demo 01
 Alternative version of CLT
 CLT  Attempt at Proof
 Implications of CLT
 Computing area under N
 Demo 02
 Special Significance for N
 Likelihood of sample mean
 SuperImpose N
 Approximating Distributions
 Demo 03
 Normal Approximation of Binomial Distribution
Chi Square Distribution
15 Lesson
3 hrs
 Chi Square Distribution
 Estimating E[S2]
 Estimating E[S2]  Exercise
 Geometric arguement
 Algebraic arguement
 Find Expected value of the error
 Estimating Var[S2]
 Distribution of sum of squares of standard normal variables
 Distribution for N>1
 k degrees of freedom
 Variance of X2(k)
 Recap & Statistics of S2
 On to Experiments
 Expectation of Proportion
 Variance of Proportion
Point and Interval Estimators
19 Lesson
4 hrs
 Point and Interval Estimators
 Examples to Solve
 What are the Estimator
 Properties of Estimator
 Point Estimator for Mean & Proportion
 Point Estimator for Sample Variance
 Example Estimation with TimeSeries
 Real World Problem
 On to Interval Estimators
 Interval Estimator of μ with known σ
 Examples of Estimator
 Examples of Estimation
 Lower and Upper Bounds
 Upper Confidence Bound
 Interval Estimator of μ with unknown σ
 T Distribution Plots
 Comparing interval bounds with z and t variables
 Examples with T Statistics
 Computing interval bounds for population proportion p
Hypothesis Testing
20 Lesson
4 hrs
 Hypothesis Testing Case Study  1
 Case Study 2
 Case Study 3 & 4
 Case Study 5 & 6
 Three Cases
 Variance: Known  Case Study 1
 Variance: Known  Case Study 2
 Effect of n, σ, and α
 Variance: Known  Case Study 3 & 4
 Variance: Known  Case Study 5 & 6
 ztest vs ttest
 Variance: Unknown  Case Study 1 & 2
 Hypothesis testing proportion(p) instead of mean
 Type 1 & Type 2 errors
 Two tailed & One tailed z test
 Two tailed & One tailed t test
 Plotting Distribution
 ChiSquare test of independence (case studies)
 ChiSquare test of independence (case study 2)
 Summary
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