Machine Learning and Java: A Practical Guide

The book "Machine Learning and Java" is a practical guide for Java developers who want to master one of the most in-demand fields in the modern IT industry. Unlike traditional courses focused on Python, this book offers a comprehensive path to implementing machine learning specifically within the Java ecosystem.

The author systematically guides the reader from basic concepts to creating production-ready ML solutions. You will start with theory: what supervised and unsupervised learning are, how decision trees, random forests, and neural networks work. Then you will move on to practical work with key Java libraries:

• Weka — for rapid prototyping and data visualization
• Deeplearning4j — for building and training deep neural networks
• Java-ML — for implementing classic clustering and classification algorithms
• Apache Spark MLlib — for processing large volumes of data

Special attention is given to practical aspects: data preprocessing, feature selection, hyperparameter tuning, and combating overfitting. You will learn how to load data from CSV files and databases, transform it into a format understandable by algorithms, and evaluate model quality using metrics: precision, recall, F1-score, ROC-AUC.

The book includes several end-to-end projects:

• Text classification (spam filter for email)
• Time series forecasting (real estate prices)
• Image recognition using convolutional networks
• Recommendation system for an online store

For each project, complete code, explanations, and optimization options are provided. You will learn not only to train models but also to integrate them into real Java applications: Spring Boot microservices, web servers, and mobile Android applications.

Who this book is for:

• Java developers looking to expand their skills in Data Science
• Technical students studying machine learning
• Team Leads and architects planning to implement ML in corporate projects
• Anyone who wants to understand how modern AI algorithms work without switching to Python

After reading the book, you will be able to independently create, train, and deploy machine learning models in Java, understand the inner workings of popular algorithms, and make informed decisions when choosing an approach for a specific task. This is not theory for theory's sake — it is a tool that can be immediately applied in your work.