Introduction to Machine Learning
Interactive Notes · 24 Chapters · Complete Solutions · Self-Paced Learning
Table of Contents
Cover Page & Preface
Chapter 01 — Course Overview
Chapter 02 — CRISP-ML, Preprocessing, EDA
↳ Case Study 01 — Credit Default Prediction
↳ Case Study 02 — Hospital Readmission Prediction
Chapter 03 — K-Nearest Neighbors
Chapter 04 — Model Evaluation & Tuning
Chapter 05 — Class Imbalance & Model Evaluation
Chapter 06 — Feature Selection
↳ Appendix — Hypothesis Testing Fundamentals
Chapter 07 — Principal Component Analysis
Chapter 08 — Naive Bayes
Chapter 09 — Decision Trees
Chapter 10 — Bagging and Random Forests
Chapter 11 — Boosting and Stacking
Chapter 12 — Imbalanced Data & Oversampling
Chapter 13 — Regression Foundations, KNN & Regression Trees
Chapter 14 — Linear Regression c Gradient Descent
Chapter 15 — Gradient Boosting and Family
Chapter 16 — Regularized Regression
Chapter 17 — Logistic Regression
Chapter 18 — Neural Networks: Introduction
Chapter 19 — Neural Networks: Backward Propagation
Chapter 20 — Neural Networks: Regularization & Architecture Design
Chapter 21 — Agglomerative Clustering
Chapter 22 — Partitional Clustering & Evaluation Metrics
Chapter 23 — DBSCAN
Chapter 24 — Explainable ML
Chapter 25 — Recommender Systems