English | 2016 | ISBN: 149196460X | 300 Pages | True PDF | 8,5 MB

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

Table of Contents
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!