Practical Machine Learning with H2O : Powerful, Scalable Techniques for Deep Learning and AI
Overview
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
Customers Also Bought
Details
- ISBN-13: 9781491964606
- ISBN-10: 149196460X
- Publisher: O'Reilly Media
- Publish Date: January 2017
- Dimensions: 9.19 x 7 x 0.63 inches
- Shipping Weight: 1.06 pounds
- Page Count: 298
Related Categories
