Chapter 1: A Process for Success

Chapter 2:Linear Regression – The Blocking and Tackling of Machine Learning

Chapter 3: Logistic Regression and Discriminant Analysis

Chapter 4: Advanced Feature Selection in Linear Models

Chapter 5: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

Chapter 6: Classification and Regression Trees

Chapter 7: Neural Networks

Chapter 8, Cluster Analysis

Chapter 9: Principal Components Analysis

Chapter 10: Market Basket Analysis and Recommendation Engines

Chapter 11: Time Series and Causality

Chapter 12: Text Mining

Appendix: R Fundamentals

Preface