Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets.

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