Copyright
Brief Table of Contents
Table of Contents
Foreword
Preface
Acknowledgments
About this Book
About the Authors
About the Cover Illustration
1. The machine-learning workflow
Chapter 1. What is machine learning?
1.1. Understanding how machines learn
1.2. Using data to make decisions
1.2.1. Traditional approaches
1.2.2. The machine-learning approach
1.2.3. Five advantages to machine learning
1.2.4. Challenges
1.3. Following the ML workflow: from data to deployment
1.3.1. Data collection and preparation
1.3.2. Learning a model from data
1.3.3. Evaluating model performance
1.3.4. Optimizing model performance
1.4. Boosting model performance with advanced techniques
1.4.1. Data preprocessing and feature engineering
1.4.2. Improving models continually with online methods
1.4.3. Scaling models with data volume and velocity
1.5. Summary
1.6. Terms from this chapter
Chapter 2. Real-world data
2.1. Getting started: data collection
2.1.1. Which features should be included?
2.1.2. How can we obtain ground truth for the target variable?
2.1.3. How much training data is required?
2.3.2. Box plots