
Introduction to Machine Learning




Lecture 1: Introduction to Machine Learning in Layman Terms  GO Classes




Annotated Notes Lecture 1 Introduction Machine Learning in Layman Terms




Lecture 2: An Optimisation View to Fit the Best Line  GO Classes




Annotated Notes Lecture 2 An Optimisation View to Fit the Best Line




Lecture 3: A Linear Algebra View to Fit the Best Line  GO Classes




Annotated Notes Lecture 3 Linear Algebra View to Fit the Best Line




Lecture 4: The First Class on Machine Learning  Simple Linear Regression




Annotated Notes Lecture 4 Machine Learning Introduction and Simple Linear Regression




Lecture 5: Many Questions on Simple Linear Regression  Multiple Linear Regression




Annotated Notes Lecture 5 Many Questions on Simple Linear Regression




Lecture 6: Multiple Linear Regression




Annotated Notes Lecture 6 Multiple Linear Regression




Lecture 7: More Questions on Multiple Linear Regression and Gradient Descent




Annotated Notes Lecture 7 More Question on Multiple Linear Regression




Lecture From Calculus Module 2 : Gradient Descent Algorithm




Annotated Notes Gradient Descent




Lecture 8: Linear Regression and Gradient Descent




Annotated Notes Lecture 8 Gradient Descent and Linear Regression




Lecture 9: 30 Questions on Batch GD, MiniBatch GD, and SGD




Annotated Notes Lecture 9: 30 Questions on Batch GD, MiniBatch GD, and SGD




Lecture 10: Polynomial Regression




Annotated Notes Lecture 10 Polynomial Regression




Lecture 11: Overfitting and Underfitting Definitions




Annotated Notes Lecture 11 OverFitting and UnderFitting




Lecture 12: Regularization and Ridge Regression




Annotated NotesLecture 12 Regularisation




Lecture 13: Ridge and LASSO Regression




Annotated NotesLecture Lecture 13 Ridge Regression




Lecture 14: Gradient Descent and More on Ridge and LASSO Regression




Annotated NotesLecture 14 Gradient Descent LAsso Ridge




Lecture 15: Logistic Regression




Annotated Notes Lecture 15 Logistic Regression




Lecture 16: More on Logistic Regression andCross Entropy Loss




Annotated Notes Lecture 16 Logistic Regression and CE loss




Lecture 17: Maximum Likelihood Estimate and Cross Entropy Loss Differentiation




Annotated Notes Lecture 17 MLE and CE loss derivative




Lecture 18 Softmax Regression or MultiClass Logistic Regression




Annotated Notes Lecture 18 Softmax Regression




Lecture 19 More about Logistic Regression  Categorical CrossEntropy Loss  More Interpretations




Annotated Notes Lecture 19 Logistic Regression




Lecture 20: KNearest Neighbors (KNN)




Annotated Notes Lecture 20 KNN




Lecture 21: Naive Bayes Classifier and Fifteen Questions on Naive Bayes




Annotated Notes Lecture 21 Naive Bayes




Lecture 22: Support Vector Machine (Hard Margin) and Twenty Questions on SVM




Annotated Notes Lecture 22 SVM




Lectures on Optimisation




Lecture 23: Soft Margin Support Vector Machine




Annotated Notes Lecture 23 Soft Margin SVM




Lecture 24: Questions on Soft SVM and Solution of Soft SVM




Annotated Notes Lecture 24 More on Soft Margin SVM




Lecture 24 (Part B): Solution of Soft SVM and Hinge Loss




Annotated Notes Lecture 24 Part 2 Hing Loss Soft Margin SVM




Lecture 25: Crossvalidation Methods (Leaveoneout (LOO) and kfolds )




Annotated Notes Lecture 25 Cross Validation




Lecture 26: 25 More Questions on Crossvalidation Method




Annotated Notes Lecture 26 More Questions on Cross Validation




Lecture 27: Precision Recall and F1 Score




Annotated Notes Lecture 27 Classification Evaluation Metrics




Lecture 28: ROC Curve AUC Classification Metric




Annotated Notes Lecture 28 ROC Curve




Lecture 29: Bias Variance Tradeoff




Annotated Notes Lecture 29 Bias Variance Tradeoff




Lecture 30: Many Questions on Bias Variance Tradeoff And Error Decomposition




Annotated Notes Lecture 30 Bias Variance Tradeoff




Lecture 31: Perceptron




Annotated Notes Lecture 31 Perceptron




Lecture 32: Principal Component Analysis (PCA)  With Geometric Interpretation  Two ways to Derive




Annotated Notes Lecture 32 PCA



Practical Notebooks




Linear Regression Practical Implementation




NoteBook Files Linear Regression




Overfitting and Underfitting




NoteBook Files Overfitting and UnderFitting




Logistic Regression Practical




NoteBook Files Logistic Regression




Polynomial Regression , Ridge & Lasso Regression




Practical Session : Polynomial Regression , Ridge and Lasso Regression




MultiClass Logistic Regression Practical




ML Practical Session : K Nearest Neighbour




Annotated Notes Lecture 14 : Linked List Insertion Deletion




Lecture 17 : Questions on Recursion




Support Vector Machine(SVM) Practical Session




ML Practical Sessions : Cross Validation Techniques




Naive Bayes Practical Implementation

