Ch 1. Introduction and Evaluation Metrics

Ch 2. Regression Models

Simple Linear Regression Multiple Linear Regression Ridge Regression

Ch 3. Classification Model I

Logistic Regression k-Nearest Neighbors Naive Bayes

Ch 4. Classification Models - II

Fisher's LDA LDA, QDA & Decision Boundaries

Ch 5. Model Evaluation and Generalization

Ch 6. Regression and Classification Models

Decision Trees Support Vector Machines Neural Networks

Chapter 4: Classification Models - II - LDA, QDA & Decision Boundaries