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Apache Mahout Essentials
Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache MahoutAbout This BookApply machine learning algorithms effectively in production environments with Apache MahoutGain better insights into large, complex, and scalable datasetsFast-paced tutorial, covering the core concepts of Apache Mahout to implement machine learning on Big DataWho This Book Is ForIf you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.
What You Will LearnGet started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applicationsUnderstand the key machine learning concepts behind algorithms in Apache MahoutApply machine learning algorithms provided by Apache Mahout in real-world practical scenariosImplement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache MahoutDiscover tips and tricks to improve the accuracy and performance of your resultsSet up Apache Mahout in a production environment with Apache HadoopGlance at the Spark DSL advancements in Apache Mahout 1.0Provide dynamic and interactive data visualizations for Apache MahoutBuild a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithmsIn DetailApache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.
This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.
Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.
What You Will LearnGet started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applicationsUnderstand the key machine learning concepts behind algorithms in Apache MahoutApply machine learning algorithms provided by Apache Mahout in real-world practical scenariosImplement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache MahoutDiscover tips and tricks to improve the accuracy and performance of your resultsSet up Apache Mahout in a production environment with Apache HadoopGlance at the Spark DSL advancements in Apache Mahout 1.0Provide dynamic and interactive data visualizations for Apache MahoutBuild a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithmsIn DetailApache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.
This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.
Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.
252 pages, Kindle Edition
First published June 19, 2015
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