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Index
Cover
Machine Learning in Action
Copyright
Dedication
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About This Book
About the Author
About the Cover Illustration
Part 1. Classification
Chapter 1. Machine learning basics
Chapter 2. Classifying with k-Nearest Neighbors
Chapter 3. Splitting datasets one feature at a time: decision trees
Chapter 4. Classifying with probability theory: naïve Bayes
Chapter 5. Logistic regression
Chapter 6. Support vector machines
Chapter 7. Improving classification with the AdaBoost meta-algorithm
Part 2. Forecasting numeric values with regression
Chapter 8. Predicting numeric values: regression
Chapter 9. Tree-based regression
Part 3. Unsupervised learning
Chapter 10. Grouping unlabeled items using k-means clustering
Chapter 11. Association analysis with the Apriori algorithm
Chapter 12. Efficiently finding frequent itemsets with FP-growth
Part 4. Additional tools
Chapter 13. Using principal component analysis to simplify data
Chapter 14. Simplifying data with the singular value decomposition
Chapter 15. Big data and MapReduce
Appendix A. Getting started with Python
Appendix B. Linear algebra
Appendix C. Probability refresher
Appendix D. Resources
Index
List of Figures
List of Tables
List of Listings
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