Packt Publishing, 2015. — 164 p. — ISBN: 1783554991, 9781783554997Apache 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.

Packt Publishing, 2015. - 164页--ISBN：1783554991, 9781783554997Apache Mahout是一个可扩展的机器学习库，具有聚类、分类和推荐的算法。它使用户能够更快、更大规模地分析大型、多样和复杂的数据集的模式。

本书是一本关于使用Apache Mahout分析大型复杂数据集的全面指南。它结合现实世界的实际例子，简单解释了复杂但非常有效的机器学习算法。

本书从机器学习和Apache Mahout的基本概念出发，引导你了解Apache Mahout对机器学习技术的实现，包括分类、聚类和推荐。在这个令人兴奋的演练过程中，对每种技术都给出了真实世界的应用、各种流行的算法及其实现、代码示例、评估策略和最佳实践。最后，你将学习Apache Mahout的vdata可视化技术，使你的数据变得生动。