| Module Name | Download | Description | Download Size |
|---|---|---|---|
| Linear Regression | Linear Algebra | Linear Algebra Tutorial | 192 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | A brief introduction to machine learning | Download |
| 2 | Supervised Learning | Download |
| 3 | Unsupervised Learning | Download |
| 4 | Reinforcement Learning | Download |
| 5 | Probability Basics - 1 | Download |
| 6 | Probability Basics - 2 | Download |
| 7 | Linear Algebra - 1 | Download |
| 8 | Linear Algebra - 2 | Download |
| 9 | Statistical Decision Theory - Regression | Download |
| 10 | Statistical Decision Theory - Classification | Download |
| 11 | Bias-Variance | Download |
| 12 | Linear Regression | Download |
| 13 | Multivariate Regression | Download |
| 14 | Subset Selection 1 | Download |
| 15 | Subset Selection 2 | Download |
| 16 | Shrinkage Methods | Download |
| 17 | Principal Components Regression | Download |
| 18 | Partial Least Squares | Download |
| 19 | Linear Classification | Download |
| 20 | Logistic Regression | Download |
| 21 | Linear Discriminant Analysis 1 | Download |
| 22 | Linear Discriminant Analysis 2 | Download |
| 23 | Linear Discriminant Analysis 3 | Download |
| 24 | Optimization | Download |
| 25 | Perceptron Learning | Download |
| 26 | SVM - Formulation | Download |
| 27 | SVM - Interpretation & Analysis | Download |
| 28 | SVMs for Linearly Non Separable Data | Download |
| 29 | SVM Kernels | Download |
| 30 | SVM - Hinge Loss Formulation | Download |
| 31 | Weka Tutorial | Download |
| 32 | Early Models | Download |
| 33 | Backpropogation I | Download |
| 34 | Backpropogation II | Download |
| 35 | Initialization, Training & Validation | Download |
| 36 | Maximum Likelihood Estimate | Download |
| 37 | Priors & MAP Estimate | Download |
| 38 | Bayesian Parameter Estimation | Download |
| 39 | Introduction | Download |
| 40 | Regression Trees | Download |
| 41 | Stopping Criteria & Pruning | Download |
| 42 | Loss Functions for Classification | Download |
| 43 | Categorical Attributes | Download |
| 44 | Multiway Splits | Download |
| 45 | Missing Values, Imputation & Surrogate Splits | Download |
| 46 | Instability, Smoothness & Repeated Subtrees | Download |
| 47 | Tutorial | Download |
| 48 | Evaluation Measures I | Download |
| 49 | Bootstrapping & Cross Validation | Download |
| 50 | 2 Class Evaluation Measures | Download |
| 51 | The ROC Curve | Download |
| 52 | Minimum Description Length & Exploratory Analysis | Download |
| 53 | Introduction to Hypothesis Testing | Download |
| 54 | Basic Concepts | Download |
| 55 | Sampling Distributions & the Z Test | Download |
| 56 | Student\'s t-test | Download |
| 57 | The Two Sample & Paired Sample t-tests | Download |
| 58 | Confidence Intervals | Download |
| 59 | Bagging, Committee Machines & Stacking | Download |
| 60 | Boosting | Download |
| 61 | Gradient Boosting | Download |
| 62 | Random Forest | Download |
| 63 | Naive Bayes | Download |
| 64 | Bayesian Networks | Download |
| 65 | Undirected Graphical Models - Introduction | Download |
| 66 | Undirected Graphical Models - Potential Functions | Download |
| 67 | Hidden Markov Models | Download |
| 68 | Variable Elimination | Download |
| 69 | Belief Propagation | Download |
| 70 | Partitional Clustering | Download |
| 71 | Hierarchical Clustering | Download |
| 72 | Threshold Graphs | Download |
| 73 | The BIRCH Algorithm | Download |
| 74 | The CURE Algorithm | Download |
| 75 | Density Based Clustering | Download |
| 76 | Gaussian Mixture Models | Download |
| 77 | Expectation Maximization | Download |
| 78 | Expectation Maximization Continued | Download |
| 79 | Spectral Clustering | Download |
| 80 | Learning Theory | Download |
| 81 | Frequent Itemset Mining | Download |
| 82 | The Apriori Property | Download |
| 83 | Introduction to Reinforcement Learning | Download |
| 84 | RL Framework and TD Learning | Download |
| 85 | Solution Methods & Applications | Download |
| 86 | Multi-class Classification | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | A brief introduction to machine learning | Download Verified |
| 2 | Supervised Learning | Download Verified |
| 3 | Unsupervised Learning | Download Verified |
| 4 | Reinforcement Learning | Download Verified |
| 5 | Probability Basics - 1 | Download Verified |
| 6 | Probability Basics - 2 | Download Verified |
| 7 | Linear Algebra - 1 | Download Verified |
| 8 | Linear Algebra - 2 | Download Verified |
| 9 | Statistical Decision Theory - Regression | Download Verified |
| 10 | Statistical Decision Theory - Classification | Download Verified |
| 11 | Bias-Variance | Download Verified |
| 12 | Linear Regression | Download Verified |
| 13 | Multivariate Regression | Download Verified |
| 14 | Subset Selection 1 | Download Verified |
| 15 | Subset Selection 2 | Download Verified |
| 16 | Shrinkage Methods | Download Verified |
| 17 | Principal Components Regression | Download Verified |
| 18 | Partial Least Squares | Download Verified |
| 19 | Linear Classification | Download Verified |
| 20 | Logistic Regression | Download Verified |
| 21 | Linear Discriminant Analysis 1 | Download Verified |
| 22 | Linear Discriminant Analysis 2 | Download Verified |
| 23 | Linear Discriminant Analysis 3 | Download Verified |
| 24 | Optimization | Download Verified |
| 25 | Perceptron Learning | Download Verified |
| 26 | SVM - Formulation | Download Verified |
| 27 | SVM - Interpretation & Analysis | Download Verified |
| 28 | SVMs for Linearly Non Separable Data | Download Verified |
| 29 | SVM Kernels | Download Verified |
| 30 | SVM - Hinge Loss Formulation | Download Verified |
| 31 | Weka Tutorial | Download Verified |
| 32 | Early Models | Download Verified |
| 33 | Backpropogation I | Download Verified |
| 34 | Backpropogation II | Download Verified |
| 35 | Initialization, Training & Validation | Download Verified |
| 36 | Maximum Likelihood Estimate | Download Verified |
| 37 | Priors & MAP Estimate | Download Verified |
| 38 | Bayesian Parameter Estimation | Download Verified |
| 39 | Introduction | Download Verified |
| 40 | Regression Trees | Download Verified |
| 41 | Stopping Criteria & Pruning | Download Verified |
| 42 | Loss Functions for Classification | Download Verified |
| 43 | Categorical Attributes | Download Verified |
| 44 | Multiway Splits | Download Verified |
| 45 | Missing Values, Imputation & Surrogate Splits | Download Verified |
| 46 | Instability, Smoothness & Repeated Subtrees | Download Verified |
| 47 | Tutorial | Download Verified |
| 48 | Evaluation Measures I | Download Verified |
| 49 | Bootstrapping & Cross Validation | Download Verified |
| 50 | 2 Class Evaluation Measures | Download Verified |
| 51 | The ROC Curve | Download Verified |
| 52 | Minimum Description Length & Exploratory Analysis | Download Verified |
| 53 | Introduction to Hypothesis Testing | Download Verified |
| 54 | Basic Concepts | Download Verified |
| 55 | Sampling Distributions & the Z Test | Download Verified |
| 56 | Student\'s t-test | Download Verified |
| 57 | The Two Sample & Paired Sample t-tests | Download Verified |
| 58 | Confidence Intervals | Download Verified |
| 59 | Bagging, Committee Machines & Stacking | Download Verified |
| 60 | Boosting | Download Verified |
| 61 | Gradient Boosting | Download Verified |
| 62 | Random Forest | Download Verified |
| 63 | Naive Bayes | Download Verified |
| 64 | Bayesian Networks | Download Verified |
| 65 | Undirected Graphical Models - Introduction | Download Verified |
| 66 | Undirected Graphical Models - Potential Functions | Download Verified |
| 67 | Hidden Markov Models | Download Verified |
| 68 | Variable Elimination | Download Verified |
| 69 | Belief Propagation | Download Verified |
| 70 | Partitional Clustering | Download Verified |
| 71 | Hierarchical Clustering | Download Verified |
| 72 | Threshold Graphs | Download Verified |
| 73 | The BIRCH Algorithm | Download Verified |
| 74 | The CURE Algorithm | Download Verified |
| 75 | Density Based Clustering | Download Verified |
| 76 | Gaussian Mixture Models | Download Verified |
| 77 | Expectation Maximization | Download Verified |
| 78 | Expectation Maximization Continued | Download Verified |
| 79 | Spectral Clustering | Download Verified |
| 80 | Learning Theory | Download Verified |
| 81 | Frequent Itemset Mining | Download Verified |
| 82 | The Apriori Property | Download Verified |
| 83 | Introduction to Reinforcement Learning | Download Verified |
| 84 | RL Framework and TD Learning | Download Verified |
| 85 | Solution Methods & Applications | Download Verified |
| 86 | Multi-class Classification | Download Verified |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Download |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |