| Module Name | Download |
|---|
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | 1.1 Paradigms of Machine Learning | Download |
| 2 | 1.2 Few more examples | Download |
| 3 | 1.3 Types of Learning | Download |
| 4 | 1.4 Types of supervised learning | Download |
| 5 | 1.5 Introduction to Regression | Download |
| 6 | 1.6 Linear regression | Download |
| 7 | 1.7 Geometrical Interpretation | Download |
| 8 | 1.8 Iterative solution: Gradient descent | Download |
| 9 | 1.9 Gradient Descent | Download |
| 10 | 1.10 Choosing Step size | Download |
| 11 | 1.11 Taylor Series | Download |
| 12 | 1.12 Stochastic Gradient Descent and basis functions | Download |
| 13 | 1.13 Regularization Techniques | Download |
| 14 | 1.14 Visual Guide to Orthogonal Projection | Download |
| 15 | 2.1 Binary Classification | Download |
| 16 | 2.2 K-Nearest Neighbour Classification | Download |
| 17 | 2.3 Distance metric and Cross-Validation | Download |
| 18 | 2.4 Computational efficiency of KNN | Download |
| 19 | 2.5 Introduction to Decision Trees | Download |
| 20 | 2.6 Level splitting | Download |
| 21 | 2.7 Measure of Impurity | Download |
| 22 | 2.8 Entropy and Information Gain | Download |
| 23 | 2.9 Generative vs Discriminative models | Download |
| 24 | 2.10 Naive Bayes classifier | Download |
| 25 | 2.11 Conditional Independence | Download |
| 26 | 2.12 Classifying the test point and summary | Download |
| 27 | 3.1 Discriminative models | Download |
| 28 | 3.2 Logistic Regression | Download |
| 29 | 3.3 Summary and big picture | Download |
| 30 | 3.4 Maximum likelihood estimation | Download |
| 31 | 3.5 Linear separability | Download |
| 32 | 3.6 Perceptron and its learning algorithm | Download |
| 33 | 3.7 Perceptron : A thing of past | Download |
| 34 | 3.8 Perceptron : A thing of past | Download |
| 35 | 3.9 Optimizing weights | Download |
| 36 | 3.10 Handling Outliers | Download |
| 37 | 3.11 Dual Formulation | Download |
| 38 | 3.12 Kernel formulation | Download |
| 39 | 4.1 Artificial Neural Networks | Download |
| 40 | 4.2 Unsupervised learning | Download |
| 41 | 4.3 K-means Clustering | Download |
| 42 | 4.4 LLyod's Algorithms | Download |
| 43 | 4.5 Convergence and Initialization | Download |
| 44 | 4.6 Representation Learning | Download |
| 45 | 4.7 Orthogonal Projection | Download |
| 46 | 4.8 Covariance Matrix and Eigen direction | Download |
| 47 | 4.9 PCA and mean centering | Download |
| 48 | 4.10 Concluding remarks | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | 1.1 Paradigms of Machine Learning | PDF unavailable |
| 2 | 1.2 Few more examples | PDF unavailable |
| 3 | 1.3 Types of Learning | PDF unavailable |
| 4 | 1.4 Types of supervised learning | PDF unavailable |
| 5 | 1.5 Introduction to Regression | PDF unavailable |
| 6 | 1.6 Linear regression | PDF unavailable |
| 7 | 1.7 Geometrical Interpretation | PDF unavailable |
| 8 | 1.8 Iterative solution: Gradient descent | PDF unavailable |
| 9 | 1.9 Gradient Descent | PDF unavailable |
| 10 | 1.10 Choosing Step size | PDF unavailable |
| 11 | 1.11 Taylor Series | PDF unavailable |
| 12 | 1.12 Stochastic Gradient Descent and basis functions | PDF unavailable |
| 13 | 1.13 Regularization Techniques | PDF unavailable |
| 14 | 1.14 Visual Guide to Orthogonal Projection | PDF unavailable |
| 15 | 2.1 Binary Classification | PDF unavailable |
| 16 | 2.2 K-Nearest Neighbour Classification | PDF unavailable |
| 17 | 2.3 Distance metric and Cross-Validation | PDF unavailable |
| 18 | 2.4 Computational efficiency of KNN | PDF unavailable |
| 19 | 2.5 Introduction to Decision Trees | PDF unavailable |
| 20 | 2.6 Level splitting | PDF unavailable |
| 21 | 2.7 Measure of Impurity | PDF unavailable |
| 22 | 2.8 Entropy and Information Gain | PDF unavailable |
| 23 | 2.9 Generative vs Discriminative models | PDF unavailable |
| 24 | 2.10 Naive Bayes classifier | PDF unavailable |
| 25 | 2.11 Conditional Independence | PDF unavailable |
| 26 | 2.12 Classifying the test point and summary | PDF unavailable |
| 27 | 3.1 Discriminative models | PDF unavailable |
| 28 | 3.2 Logistic Regression | PDF unavailable |
| 29 | 3.3 Summary and big picture | PDF unavailable |
| 30 | 3.4 Maximum likelihood estimation | PDF unavailable |
| 31 | 3.5 Linear separability | PDF unavailable |
| 32 | 3.6 Perceptron and its learning algorithm | PDF unavailable |
| 33 | 3.7 Perceptron : A thing of past | PDF unavailable |
| 34 | 3.8 Perceptron : A thing of past | PDF unavailable |
| 35 | 3.9 Optimizing weights | PDF unavailable |
| 36 | 3.10 Handling Outliers | PDF unavailable |
| 37 | 3.11 Dual Formulation | PDF unavailable |
| 38 | 3.12 Kernel formulation | PDF unavailable |
| 39 | 4.1 Artificial Neural Networks | PDF unavailable |
| 40 | 4.2 Unsupervised learning | PDF unavailable |
| 41 | 4.3 K-means Clustering | PDF unavailable |
| 42 | 4.4 LLyod's Algorithms | PDF unavailable |
| 43 | 4.5 Convergence and Initialization | PDF unavailable |
| 44 | 4.6 Representation Learning | PDF unavailable |
| 45 | 4.7 Orthogonal Projection | PDF unavailable |
| 46 | 4.8 Covariance Matrix and Eigen direction | PDF unavailable |
| 47 | 4.9 PCA and mean centering | PDF unavailable |
| 48 | 4.10 Concluding remarks | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 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 |