Includes index.

Chapter 1. Introduction to machine learning -- Chapter 2. Machine learning and large-scale datasets -- Chapter 3. An introduction to Hadoop's architecture and ecosystem -- Chapter 4. Machine learning tools, libraries, and frameworks -- Chapter 5. Decision tree based learning -- Chapter 6. Instance and Kernel methods based learning -- Chapter 7. Association rules based learning -- Chapter 8. Clustering based learning -- Chapter 9. Bayesian learning -- Chapter 10 : Regression based learning -- Chapter 11. Deep learning -- Chapter 12. Reinforcement learning -- Chapter 13. Ensemble learning -- Chapter 14. New generation data architectures for machine learning.

This book explores an extensive range of machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.