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

by Darren Cook

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. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.

Learn how to import, manipulate, and export data with H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work

Genres: Computers, Technical

302 pages, Kindle Edition

Published December 5, 2016

Reviews

[Wojciech · Rating 4 out of 5]

Very practical book that goes through the features of H2O, it's that and only that. It doesn't go into the theory of how the algorithms work or anything like that.

Recommended as tutorial on how to use H2O without going into what's going on behind the scenes.

I'd love to see new, updated release that shows all new features of H2O that were released since the publication.

[Jin Shusong · Rating 5 out of 5]

Really helpful when using h2o as the machine learning framework. The author also tells us his parameter tuning skills.