Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

In Practical Machine Learning with H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H2O, import and export data, and distinguish H2O algorithms. Readers will then explore various modern machine learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Throughout the chapters, machine learning models are introduced and tried on the same 3 data sets, guiding readers through the process of finding the right parameters for a given data set.

Table of Contents

Chapter 1. Installation and Quick-Start
Chapter 2. Data Import, Data Export
Chapter 3. The Data Sets
Chapter 4. Common Model Parameters
Chapter 5. Random Forest
Chapter 6. Gradient Boosting Machines
Chapter 7. Linear Models
Chapter 8. Deep Learning (Neural Nets)
Chapter 9. Unsupervised Learning
Chapter 10. Everything Else
Chapter 11. Epilogue: Didn’t They All Do Well!