Apache Mahout is a scalable machine learning library that provides algorithms for classification, clustering, and recommendations.
This book helps you to use Apache Mahout to implement widely used machine learning algorithms in order to gain better insights about large and complex datasets in a scalable manner.
Starting from fundamental concepts in machine learning and Apache Mahout, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each machine learning technique. Further, this book contains a complete step-by-step guide to set up Apache Mahout in the production environment, using Apache Hadoop to unleash the scalable power of Apache Mahout in a distributed environment. Finally, you are guided toward the data visualization techniques for Apache Mahout, which make your data come alive!
Chapter 1, Introducing Apache Mahout, provides an introduction to machine learning and Apache Mahout.
Chapter 2, Clustering, provides an introduction to unsupervised learning and clustering techniques (K-Means clustering and other algorithms) in Apache Mahout along with performance optimization tips for clustering.
Chapter 3, Regression and Classification, provides an introduction to supervised learning and classification techniques (linear regression, logistic regression, Naïve Bayes, and HMMs) in Apache Mahout.
Chapter 4, Recommendations, provides a comparison between collaborative- and content-based filtering and recommenders in Apache Mahout (user-based, item-based, and matrix-factorization-based).
Chapter 5, Apache Mahout in Production, provides a guide to scaling Apache Mahout in the production environment with Apache Hadoop.
Chapter 6, Visualization, provides a guide to visualizing data using D3.js.
The following software libraries are needed at various phases of this book:
Java 1.7 or above
Apache Mahout
Apache Hadoop
Apache Spark
D3.js
If you are a Java developer or a data scientist who has not worked with Apache Mahout previously and want to get up to speed on implementing machine learning on big data, then this is a concise and fast-paced guide for you.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Save the following content in a file named as KmeansTest.data."
A block of code is set as follows:
<dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-core</artifactId>
<version>${mahout.version}</version>
</dependency>When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
private static final String DIRECTORY_CONTAINING_CONVERTED_INPUT = "Kmeansdata";Any command-line input or output is written as follows:
mahout seq2sparse -i kmeans/sequencefiles -o kmeans/sparse
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