Summary
Mahout in Action is a hands-on introduction to machine learning with
Apache Mahout. Following real-world examples, the book presents
practical use cases and then illustrates how Mahout can be applied to
solve them. Includes a free audio- and video-enhanced ebook.
About the Technology
A computer system that learns and adapts as it collects data can be
really powerful. Mahout, Apache's open source machine learning project,
captures the core algorithms of recommendation systems, classification,
and clustering in ready-to-use, scalable libraries. With Mahout, you can
immediately apply to your own projects the machine learning techniques
that drive Amazon, Netflix, and others.
About this Book
This book covers machine learning using Apache Mahout. Based on
experience with real-world applications, it introduces practical use
cases and illustrates how Mahout can be applied to solve them. It places
particular focus on issues of scalability and how to apply these
techniques against large data sets using the Apache Hadoop framework.
This book is written for developers familiar with Java -- no prior
experience with Mahout is assumed.
Owners of a Manning pBook purchased anywhere in the world can download a
free eBook from manning.com at any time. They can do so multiple times
and in any or all formats available (PDF, ePub or Kindle). To do so,
customers must register their printed copy on Manning's site by creating
a user account and then following instructions printed on the pBook
registration insert at the front of the book.
What's Inside
- Use group data to make individual recommendations
- Find logical clusters within your data
- Filter and refine with on-the-fly classification
- Free audio and video extras
Table of Contents
- Meet Apache Mahout
- Introducing recommenders
- Representing recommender data
- Making recommendations
- Taking recommenders to production
- Distributing recommendation computations
- Introduction to clustering
- Representing data
- Clustering algorithms in Mahout
- Evaluating and improving clustering quality
- Taking clustering to production
- Real-world applications of clustering
- Introduction to classification
- Training a classifier
- Evaluating and tuning a classifier
- Deploying a classifier
- Case study: Shop It To Me
