Introduction to Machine Learning
Interactive Lecture Notes · Complete Solutions · Self-Paced Learning
Table of Contents
Cover Page & Preface
Unit 01 - Course Overview
Unit 02 - CRISP-ML, Preprocessing, EDA
Unit 03 - Standard and Weighted KNN
Unit 04 - Hold Out & K-Fold CV
Unit 05 - Over/Underfitting & F1-Score
Unit 06 - ROC Curve & Dimensionality
Unit 07 - Feature Selection (Filter)
Unit 08 - Wrapper Methods & PCA
Unit 09 - PCA Contd & Naive Bayes
Unit 10 - Naive Bayes Contd
Unit 11 - Decision Trees
Unit 12 - Bootstrapping & Ensembles
Unit 13 - Boosting & Adaboost
Unit 14 - Stacking & Oversampling
Unit 15 - KNN Regressor & Eval Metrics
Unit 16 - Linear Regression
Unit 17 - Multiple Regression & Features
Unit 18 - Regularized Regression
Unit 19 - Logistic Regression
Unit 20 - Neural Networks
Unit 21 - Neural Networks (Backward)
Unit 22 - Neural Networks Contd
Unit 23 - NN Demo & Agglomerative
Unit 24 - K-Means Clustering
Unit 25 - Clustering Eval & DBSCAN
Unit 26 - Clustering Demo & XAI
Unit 27 - Explainable ML (LIME)
Unit 28 - Recommender Systems