Practical Machine Learning with H20: Powerful, Scalable Techniques for Deep Learning and AI

by Darren Cook

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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
  • ISBN10 149196460X
  • ISBN13 9781491964606
  • Publish Date 10 January 2017
  • Publish Status Active
  • Out of Print 1 October 2024
  • Publish Country US
  • Imprint O'Reilly Media
  • Format Paperback
  • Pages 300
  • Language English