Practical Machine Learning with H2O

Practical Machine Learning with H2O

Powerful, Scalable Techniques for Deep Learning and AI

by Darren Cook

Year: 2016

Pages: 374

Publisher: O'Reilly Media

ISBN: 978-1491964606, 149196460X

Description

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

Tags

Algorithms Big Data Data Analysis Machine Learning Math Python R Statistics Theory

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