Includes bibliographical references and index.

What is machine learning? -- Planning machine learning -- Working with decision trees -- Bayesian networks -- Artificial neural networks -- Association rules learning -- Support vector machines -- Clustering -- Machine learning in real time with Spring XD -- Maching learning as a batch process -- Apache Spark -- Machine learning with R.

This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. It contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. It is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: learn the languages of machine learning including Hadoop, Mahout, and Weka; understand decision trees, Bayesian networks, and artificial neural networks; implement association rule, real time, and batch learning; develop a strategic plan for safe, effective, and efficient machine learning. -- Edited summary