PART I PRELIMINARIES
CHAPTER 1 Introduction
CHAPTER 2 Overview of the Machine Learning Process

PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization
CHAPTER 4 Dimension Reduction

PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance

PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression
CHAPTER 7 k-Nearest Neighbors (k-NN)
CHAPTER 8 The Naive Bayes Classifier
CHAPTER 9 Classification and Regression Trees
CHAPTER 10 Logistic Regression
CHAPTER 11 Neural Networks
CHAPTER 12 Discriminant Analysis
CHAPTER 13 Generating, Comparing, and Combining Multiple Models

PART V INTERVENTION AND USER FEEDBACK
CHAPTER 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning

PART VI MINING RELATIONSHIPS AMONG RECORDS
CHAPTER 15 Association Rules and Collaborative Filtering
CHAPTER 16 Cluster Analysis

PART VII FORECASTING TIME SERIES
CHAPTER 17 Handling Time Series
CHAPTER 18 Regression-Based Forecasting
CHAPTER 19 Smoothing and Deep Learning Methods for Forecasting

PART VIII DATA ANALYTICS
CHAPTER 20 Social Network Analytics
CHAPTER 21 Text Mining
CHAPTER 22 Responsible Data Science

PART IX CASES
CHAPTER 23 Cases