Lecture 1 - Introduction to AI/ML/DS
Lecture 2 - Introduction to Probability; Introduction to machine learning - Part 1
Lecture 3 - Introduction to Probability; Introduction to machine learning - Part 2
Lecture 4 - Introduction to Probability; Introduction to machine learning - Part 3
Lecture 5 - Introduction to Probability; Introduction to machine learning - Part 4
Lecture 6 - Python for AI/ML/DS - Part 1
Lecture 7 - Python for AI/ML/DS - Part 2
Lecture 8 - Descriptive statistics and Inferential statistics - Part 1
Lecture 9 - Descriptive statistics and Inferential statistics - Part 2
Lecture 10 - Descriptive statistics and Inferential statistics - Part 3
Lecture 11 - Descriptive statistics and Inferential statistics - Part 4
Lecture 12 - Descriptive statistics and Inferential statistics - Part 5
Lecture 13 - Distribution, Data visualization, Plotting libraries - Part 1
Lecture 14 - Distribution, Data visualization, Plotting libraries - Part 2
Lecture 15 - Distribution, Data visualization, Plotting libraries - Part 3
Lecture 16 - Linear Algebra for Data science
Lecture 17 - Identification of linear relationship among attributes
Lecture 18 - Solving Linear Equations - 1
Lecture 19 - Solving Linear Equations - 2
Lecture 20 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors - Part 1
Lecture 21 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors - Part 2
Lecture 22 - Linear Algebra - Part 1
Lecture 23 - Linear Algebra - Part 2
Lecture 24 - Linear Algebra - Part 3
Lecture 25 - Regression Models, Models Selection and Evaluation - Part 1
Lecture 26 - Regression Models, Models Selection and Evaluation - Part 2
Lecture 27 - Regression Models, Models Selection and Evaluation - Part 3
Lecture 28 - Regression Models, Models Selection and Evaluation - Part 4
Lecture 29 - Regression - Part 1
Lecture 30 - Regression - Part 2
Lecture 31 - Regression - Part 3
Lecture 32 - Classification Naive Bayes, Logistic Regression, K-NN - Part 1
Lecture 33 - Classification Naive Bayes, Logistic Regression, K-NN - Part 2
Lecture 34 - Classification Naive Bayes, Logistic Regression, K-NN - Part 3
Lecture 35 - Classification Naive Bayes, Logistic Regression, K-NN - Part 4
Lecture 36 - Classification - Part 1
Lecture 37 - Classification - Part 2
Lecture 38 - Classification - Part 3
Lecture 39 - Linear Models for Classification - Part 1
Lecture 40 - Linear Models for Classification - Part 2
Lecture 41 - Kernel Machines
Lecture 42 - Solving Langrange Dual in SVM
Lecture 43 - Classification and SVM - Part 1
Lecture 44 - Classification and SVM - Part 2
Lecture 45 - Tree - Based methods, Boosting bagging - Part 1
Lecture 46 - Tree - Based methods, Boosting bagging - Part 2
Lecture 47 - Tree - Based methods, Boosting bagging - Part 3
Lecture 48 - Tree - Based methods, Boosting bagging - Part 4
Lecture 49 - Tree-based approaches for regression and classification - Part 1
Lecture 50 - Tree-based approaches for regression and classification - Part 2
Lecture 51 - Supervised Learning Using K Nearest Neighbors - Part 1
Lecture 52 - Supervised Learning Using K Nearest Neighbors - Part 2
Lecture 53 - Supervised Learning Using K Nearest Neighbors - Part 3
Lecture 54 - Supervised Learning Using K Nearest Neighbors - Part 4
Lecture 55 - Clustering methods - Part 1
Lecture 56 - Clustering methods - Part 2
Lecture 57 - Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks - Part 1
Lecture 58 - Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks - Part 2
Lecture 59 - Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks - Part 3
Lecture 60 - Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks - Part 4
Lecture 61 - Neural Networks and Feedforward NN - Part 1
Lecture 62 - Neural Networks and Feedforward NN - Part 2
Lecture 63 - Neural Networks and Feedforward NN - Part 3
Lecture 64 - Backpropagation (Intuition)
Lecture 65 - Backpropagation: Computing Cradients w.r.t the Output Units
Lecture 66 - Learning Parameters: Gradient Descent
Lecture 67 - Contours
Lecture 68 - Nesterov Accelerated Gradient Descent
Lecture 69 - Stochastic and Mini-Batch Gradient Descent
Lecture 70 - Tips for Adjusting learning Rate and Momentum
Lecture 71 - Line Search
Lecture 72 - The convolution operation
Lecture 73 - Convolutional Neural Networks
Lecture 74 - CNN and DL models - Part 1
Lecture 75 - CNN and DL models - Part 2
Lecture 76 - CNN and DL models - Part 3
Lecture 77 - CNN and DL models - Part 4
Lecture 78 - AI/ML/DS Industry Use Cases - Part 1
Lecture 79 - AI/ML/DS Industry Use Cases - Part 2
Lecture 80 - AI/ML - Case Studies in Industry - Part 1
Lecture 81 - AI/ML - Case Studies in Industry - Part 2
Lecture 82 - Q and A on career in research a woman faculty representative from PSGTech and RBCDSAI
