## Book 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

## Publisher resources

## Table of contents

- Preface
- 1. Installation and Quick-Start
- 2. Data Import, Data Export
- 3. The Data Sets
- 4. Common Model Parameters
- 5. Random Forest
- 6. Gradient Boosting Machines
- 7. Linear Models
- 8. Deep Learning (Neural Nets)
- 9. Unsupervised Learning
- 10. Everything Else
- 11. Epilogue: Didn’t They All Do Well!
- Index

## Product information

- Title: Practical Machine Learning with H2O
- Author(s):
- Release date: December 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491964606

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