Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

Cook, Darren

ISBN 10: 149196460X / ISBN 13: 9781491964606

Published by O'Reilly Media, 2017

About this Item

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Seller Inventory # GOR009379730

Bibliographic Details

• Title: Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
• Publisher: O'Reilly Media
• Publication Date: 2017
• Language: English
• ISBN 10: 149196460X
• ISBN 13: 9781491964606
• Binding: Paperback
• Condition: Very Good

About this title

Synopsis

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

About the Author

Darren Cook has over 20 years of experience as a software developer, data analyst, and technical director, working on everything from financial trading systems to NLP, data visualization tools, and PR websites for some of the world’s largest brands. He is skilled in a wide range of computer languages, including R, C++, PHP, JavaScript, and Python. He works at QQ Trend, a financial data analysis and data products company.