Table of Contents

Introduction

Lesson 1: Fundamental Classification Methods
Topics
1.1 Revisiting Classification
1.2 Decision Trees I
1.3 Decision Trees II
1.4 Support Vector Classifiers I
1.5 Support Vector Classifiers II

Lesson 2: Fundamental Classification Methods I
Topics
2.1 Logistic Regression I
2.2 Logistic Regression II
2.3 Discriminant Analysis I
2.4 Discriminant Analysis II
2.5 Bias and Variance of Classifiers
2.6 Comparing Classifiers

Lesson 3: Fundamental Regression Methods
Topics
3.1 Penalized Regression I
3.2 Penalized Regression II
3.3 Piecewise Constant Regression
3.4 Regression Trees
3.5 Bias and Variance of Regressors
3.6 Comparing Regressors

Lesson 4: Manual Feature Engineering
Topics
4.1 Overview of Feature Engineering
4.2 Feature Scaling
4.3 Discretization
4.4 Categorical Coding
4.5 Interactions
4.6 Target Manipulations

Lesson 5: Hyperparameters and Pipelines
Topics
5.1 Models, Parameters, and Hyperparameters
5.2 Tuning Hyperparameters
5.3 Nested Cross-validation
5.4 Pipelines
5.5 Tuning Pipelines

Summary

About the Author

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.