Amazon.com: Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI eBook: Darren Cook: Kindle Store

This seems to be the very first book on this ML framework (H2O). And is is just great. The book is crystal clear and extremely comprehensive, very easy to read, with examples you can reproduce easily (datasets are on line in a public Git repo). It covers a very practical ground on the 4 main algorithms implemented in H2O cluster: RandomForest, GBM, GLM, and last but not least: deep learning... "Practical" means explanations are strongly grounded on a set of 4 datasets, the author plays with, explaining both their preparation, analysis with H2O (code is both in R and PYTHON), and a great deal of time is spent on very useful considerations on how to'tune' the various algorithms to obtain better models, comparing their effectiveness. A must have for everyone interested in implementing ML features concretely.