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 H2O
Explore key machine-learning concepts, such as cross-validation and validation data sets
Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification
Use H2O to analyze each sample data set with four supervised machine-learning algorithms
Understand how cluster analysis and other unsupervised machine-learning algorithms work

�ber die Autorin bzw. den Autor

Darren Cook has over 20 years experience as a software developer and technical director, working on everything from financial trading systems, through data visualization tools, through PR websites for some of the world's largest brands, all the way to arcade games. He is skilled in a wide range of computer languages, including Javascript, PHP and C++. He has developed systems around http streaming web services, such as Twitter, written many low-level direct socket server/client protocols in numerous applications, and built applications with websockets.

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