Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
Apache 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 Learn
Get started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applications
Understand the key machine learning concepts behind algorithms in Apache Mahout
Apply machine learning algorithms provided by Apache Mahout in real-world practical scenarios
Implement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache Mahout
Discover tips and tricks to improve the accuracy and performance of your results
Set up Apache Mahout in a production environment with Apache Hadoop
Glance at the Spark DSL advancements in Apache Mahout 1.0
Provide dynamic and interactive data visualizations for Apache Mahout
Build a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithms
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