| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction to AI/ML/DS | Download |
| 2 | Introduction to Probability; Introduction to machine learning Part 1 | Download |
| 3 | Introduction to Probability; Introduction to machine learning Part 2 | Download |
| 4 | Introduction to Probability; Introduction to machine learning Part 3 | Download |
| 5 | Introduction to Probability; Introduction to machine learning Part 4 | Download |
| 6 | Python for AI/ML/DS Part 1 | Download |
| 7 | Python for AI/ML/DS Part 2 | Download |
| 8 | Descriptive statistics and Inferential statistics Part 1 | Download |
| 9 | Descriptive statistics and Inferential statistics Part 2 | Download |
| 10 | Descriptive statistics and Inferential statistics Part 3 | Download |
| 11 | Descriptive statistics and Inferential statistics Part 4 | Download |
| 12 | Descriptive statistics and Inferential statistics Part 5 | Download |
| 13 | Distribution, Data visualization, Plotting libraries Part 1 | Download |
| 14 | Distribution, Data visualization, Plotting libraries Part 2 | Download |
| 15 | Distribution, Data visualization, Plotting libraries Part 3 | Download |
| 16 | Linear Algebra for Data science | Download |
| 17 | Identification of linear relationship among attributes | Download |
| 18 | Solving Linear Equations 1 | Download |
| 19 | Solving Linear Equations 2 | Download |
| 20 | Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 1 | Download |
| 21 | Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 2 | Download |
| 22 | Linear Algebra Part 1 | Download |
| 23 | Linear Algebra Part 2 | Download |
| 24 | Linear Algebra Part 3 | Download |
| 25 | Regression Models, Models Selection & Evaluation Part 1 | Download |
| 26 | Regression Models, Models Selection & Evaluation Part 2 | Download |
| 27 | Regression Models, Models Selection & Evaluation Part 3 | Download |
| 28 | Regression Models, Models Selection & Evaluation Part 4 | Download |
| 29 | Regression Part 1 | Download |
| 30 | Regression Part 2 | Download |
| 31 | Regression Part 3 | Download |
| 32 | Classification Naive Bayes, Logistic Regression, K-NN Part 1 | Download |
| 33 | Classification Naive Bayes, Logistic Regression, K-NN Part 2 | Download |
| 34 | Classification Naive Bayes, Logistic Regression, K-NN Part 3 | Download |
| 35 | Classification Naive Bayes, Logistic Regression, K-NN Part 4 | Download |
| 36 | Classification - Part 1 | Download |
| 37 | Classification - Part 2 | Download |
| 38 | Classification - Part 3 | Download |
| 39 | Linear Models for Classification Part 1 | Download |
| 40 | Linear Models for Classification Part 2 | Download |
| 41 | Kernel Machines | Download |
| 42 | Solving Langrange Dual in SVM | Download |
| 43 | Classification and SVM Part 1 | Download |
| 44 | Classification and SVM Part 2 | Download |
| 45 | Tree - Based methods, Boosting bagging Part 1 | Download |
| 46 | Tree - Based methods, Boosting bagging Part 2 | Download |
| 47 | Tree - Based methods, Boosting bagging Part 3 | Download |
| 48 | Tree - Based methods, Boosting bagging Part 4 | Download |
| 49 | Tree-based approaches for regression and classification Part 1 | Download |
| 50 | Tree-based approaches for regression and classification Part 2 | Download |
| 51 | Supervised Learning Using K Nearest Neighbors Part 1 | Download |
| 52 | Supervised Learning Using K Nearest Neighbors Part 2 | Download |
| 53 | Supervised Learning Using K Nearest Neighbors Part 3 | Download |
| 54 | Supervised Learning Using K Nearest Neighbors Part 4 | Download |
| 55 | Clustering methods Part 1 | Download |
| 56 | Clustering methods Part 2 | Download |
| 57 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part1 | Download |
| 58 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part2 | Download |
| 59 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part3 | Download |
| 60 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part4 | Download |
| 61 | Neural Networks and Feedforward NN Part 1 | Download |
| 62 | Neural Networks and Feedforward NN Part 2 | Download |
| 63 | Neural Networks and Feedforward NN Part 3 | Download |
| 64 | Backpropagation (Intuition) | Download |
| 65 | Backpropagation: Computing Cradients w.r.t the Output Units | Download |
| 66 | Learning Parameters: Gradient Descent | Download |
| 67 | Contours | Download |
| 68 | Nesterov Accelerated Gradient Descent | Download |
| 69 | Stochastic and Mini-Batch Gradient Descent | Download |
| 70 | Tips for Adjusting learning Rate and Momentum | Download |
| 71 | Line Search | Download |
| 72 | The convolution operation | Download |
| 73 | Convolutional Neural Networks | Download |
| 74 | CNN and DL models Part 1 | Download |
| 75 | CNN and DL models Part 2 | Download |
| 76 | CNN and DL models Part 3 | Download |
| 77 | CNN and DL models Part 4 | Download |
| 78 | AI/ML/DS Industry Use Cases Part 1 | Download |
| 79 | AI/ML/DS Industry Use Cases Part 2 | Download |
| 80 | AI/ML - Case Studies in Industry Part 1 | Download |
| 81 | AI/ML - Case Studies in Industry Part 2 | Download |
| 82 | "Q&A on career in research a woman faculty representative from PSGTech and RBCDSAI " | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction to AI/ML/DS | PDF unavailable |
| 2 | Introduction to Probability; Introduction to machine learning Part 1 | PDF unavailable |
| 3 | Introduction to Probability; Introduction to machine learning Part 2 | PDF unavailable |
| 4 | Introduction to Probability; Introduction to machine learning Part 3 | PDF unavailable |
| 5 | Introduction to Probability; Introduction to machine learning Part 4 | PDF unavailable |
| 6 | Python for AI/ML/DS Part 1 | PDF unavailable |
| 7 | Python for AI/ML/DS Part 2 | PDF unavailable |
| 8 | Descriptive statistics and Inferential statistics Part 1 | PDF unavailable |
| 9 | Descriptive statistics and Inferential statistics Part 2 | PDF unavailable |
| 10 | Descriptive statistics and Inferential statistics Part 3 | PDF unavailable |
| 11 | Descriptive statistics and Inferential statistics Part 4 | PDF unavailable |
| 12 | Descriptive statistics and Inferential statistics Part 5 | PDF unavailable |
| 13 | Distribution, Data visualization, Plotting libraries Part 1 | PDF unavailable |
| 14 | Distribution, Data visualization, Plotting libraries Part 2 | PDF unavailable |
| 15 | Distribution, Data visualization, Plotting libraries Part 3 | PDF unavailable |
| 16 | Linear Algebra for Data science | PDF unavailable |
| 17 | Identification of linear relationship among attributes | PDF unavailable |
| 18 | Solving Linear Equations 1 | PDF unavailable |
| 19 | Solving Linear Equations 2 | PDF unavailable |
| 20 | Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 1 | PDF unavailable |
| 21 | Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 2 | PDF unavailable |
| 22 | Linear Algebra Part 1 | PDF unavailable |
| 23 | Linear Algebra Part 2 | PDF unavailable |
| 24 | Linear Algebra Part 3 | PDF unavailable |
| 25 | Regression Models, Models Selection & Evaluation Part 1 | PDF unavailable |
| 26 | Regression Models, Models Selection & Evaluation Part 2 | PDF unavailable |
| 27 | Regression Models, Models Selection & Evaluation Part 3 | PDF unavailable |
| 28 | Regression Models, Models Selection & Evaluation Part 4 | PDF unavailable |
| 29 | Regression Part 1 | PDF unavailable |
| 30 | Regression Part 2 | PDF unavailable |
| 31 | Regression Part 3 | PDF unavailable |
| 32 | Classification Naive Bayes, Logistic Regression, K-NN Part 1 | PDF unavailable |
| 33 | Classification Naive Bayes, Logistic Regression, K-NN Part 2 | PDF unavailable |
| 34 | Classification Naive Bayes, Logistic Regression, K-NN Part 3 | PDF unavailable |
| 35 | Classification Naive Bayes, Logistic Regression, K-NN Part 4 | PDF unavailable |
| 36 | Classification - Part 1 | PDF unavailable |
| 37 | Classification - Part 2 | PDF unavailable |
| 38 | Classification - Part 3 | PDF unavailable |
| 39 | Linear Models for Classification Part 1 | PDF unavailable |
| 40 | Linear Models for Classification Part 2 | PDF unavailable |
| 41 | Kernel Machines | PDF unavailable |
| 42 | Solving Langrange Dual in SVM | PDF unavailable |
| 43 | Classification and SVM Part 1 | PDF unavailable |
| 44 | Classification and SVM Part 2 | PDF unavailable |
| 45 | Tree - Based methods, Boosting bagging Part 1 | PDF unavailable |
| 46 | Tree - Based methods, Boosting bagging Part 2 | PDF unavailable |
| 47 | Tree - Based methods, Boosting bagging Part 3 | PDF unavailable |
| 48 | Tree - Based methods, Boosting bagging Part 4 | PDF unavailable |
| 49 | Tree-based approaches for regression and classification Part 1 | PDF unavailable |
| 50 | Tree-based approaches for regression and classification Part 2 | PDF unavailable |
| 51 | Supervised Learning Using K Nearest Neighbors Part 1 | PDF unavailable |
| 52 | Supervised Learning Using K Nearest Neighbors Part 2 | PDF unavailable |
| 53 | Supervised Learning Using K Nearest Neighbors Part 3 | PDF unavailable |
| 54 | Supervised Learning Using K Nearest Neighbors Part 4 | PDF unavailable |
| 55 | Clustering methods Part 1 | PDF unavailable |
| 56 | Clustering methods Part 2 | PDF unavailable |
| 57 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part1 | PDF unavailable |
| 58 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part2 | PDF unavailable |
| 59 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part3 | PDF unavailable |
| 60 | Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part4 | PDF unavailable |
| 61 | Neural Networks and Feedforward NN Part 1 | PDF unavailable |
| 62 | Neural Networks and Feedforward NN Part 2 | PDF unavailable |
| 63 | Neural Networks and Feedforward NN Part 3 | PDF unavailable |
| 64 | Backpropagation (Intuition) | PDF unavailable |
| 65 | Backpropagation: Computing Cradients w.r.t the Output Units | PDF unavailable |
| 66 | Learning Parameters: Gradient Descent | PDF unavailable |
| 67 | Contours | PDF unavailable |
| 68 | Nesterov Accelerated Gradient Descent | PDF unavailable |
| 69 | Stochastic and Mini-Batch Gradient Descent | PDF unavailable |
| 70 | Tips for Adjusting learning Rate and Momentum | PDF unavailable |
| 71 | Line Search | PDF unavailable |
| 72 | The convolution operation | PDF unavailable |
| 73 | Convolutional Neural Networks | PDF unavailable |
| 74 | CNN and DL models Part 1 | PDF unavailable |
| 75 | CNN and DL models Part 2 | PDF unavailable |
| 76 | CNN and DL models Part 3 | PDF unavailable |
| 77 | CNN and DL models Part 4 | PDF unavailable |
| 78 | AI/ML/DS Industry Use Cases Part 1 | PDF unavailable |
| 79 | AI/ML/DS Industry Use Cases Part 2 | PDF unavailable |
| 80 | AI/ML - Case Studies in Industry Part 1 | PDF unavailable |
| 81 | AI/ML - Case Studies in Industry Part 2 | PDF unavailable |
| 82 | "Q&A on career in research a woman faculty representative from PSGTech and RBCDSAI " | 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 |