Practical Machine Learning with H2O (eBook)

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  • 86,873 Words
  • 300 Pages

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

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


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