Machine Learning with Java

This book is an introductory guide to machine learning for Java developers. It first lays out the basic concepts of machine learning and data science, explaining when a problem is well suited to a machine learning solution and walking through the complete preparatory pipeline—from data collection and cleaning, missing value imputation, and outlier handling to feature transformation and reduction. It then surveys the machine learning libraries and platforms available in the Java ecosystem, including Weka, Apache Mahout, Apache Spark, Deeplearning4j, and MALLET, comparing the scenarios each is suited to. At the algorithmic level, the book covers foundational tasks such as classification, regression, and clustering, and also touches on application areas including ensemble methods, association analysis, recommendation, anomaly and fraud detection, image recognition, sensor-based activity recognition, and text mining. Each chapter centers on a specific task, presenting the approach, tool selection, and an example workflow; readers can consult chapters as needed or read straight through following the thread of data processing. At roughly 170 pages, the book moves at a brisk pace, focusing on getting readers up and running with Java machine learning libraries rather than deriving formulas. It is well suited as a first hands-on reference for readers with a Java background who are just entering the field of machine learning.