Originally posted on Data Science Central

This article introduces Mahout, a library for scalable machine learning, and studies potential applications through two Mahout projects. It was written by Linda Terlouw. Linda is a computer scientist who works on Data Science (Data Analysis, Data Visualization, Process Mining).

Apache Mahout is a library for scalable machine learning. Originally a subproject of Apache Lucene (a high-performance text search engine library), Mahout has progressed to be a top-level Apache project.

While Mahout has only been around for a few years, it has established itself as a frontrunner in the field of machine learning technologies. Mahout has currently been adopted by: Foursquare, which uses Mahout with Apache Hadoop and Apache Hiveto power its recommendation engine; Twitter, which creates user interest models using Mahout; and Yahoo!, which uses Mahout in their anti-spam analytic platform. Other commercial and academic uses of Mahout have been catalogued at https://mahout.apache.org/general/powered-by-mahout.html.

This Refcard will present the basics of Mahout by studying two possible applications:

Training and testing a Random Forest for handwriting recognition using Amazon Web Services EMR AND
Running a recommendation engine on a standalone Spark cluster.

In this article there are 10 sections:

Introduction
Machine Learning
Algorithms Supported in Apache Mahout
Installing Apache Mahout
Example of Multi-Class Classification Using Amazon Elastic MapReduce
Getting and Preparing the Data
Classifying From Command Line Using Amazon Elastic MapReduce
Interpreting the Test Results
Using Apache Mahout With Apache Spark for Recommendations
Running Mahout from Java or Scala

To check out all this information, click here.

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Source: Distributed Machine Learning with Apache Mahout