Practical Machine Learning With H2O: Powerful, Scalable Techniques For Deep Learning And Ai
- Publish Date: 2016-12-16
- Binding: Paperback
- Author: 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 thats 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 youre 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. Youll 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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