Machine Learning and Java: From Basics to Production

The book "Machine Learning and Java" is a comprehensive practical guide for Java developers who want to master machine learning and deploy ML models in real projects. Unlike many books focused on Python, this edition focuses exclusively on the Java ecosystem: libraries, frameworks, and tools available to JVM developers.

What is this book about?

The book covers the full cycle of working with machine learning in Java:

• ML Basics: linear regression, decision trees, SVM, clustering — with examples in Java.
• Libraries and Frameworks: Deeplearning4j (neural networks), Weka (classical algorithms), Smile, Tribuo, Apache Spark MLlib.
• Data Preparation: working with CSV, JSON, databases, data cleaning and feature engineering.
• Deep Learning: convolutional networks (CNN) for images, recurrent networks (RNN) for text, transformers.
• NLP and Computer Vision: natural language processing (Stanford CoreNLP, OpenNLP), image recognition (JavaCV, DL4J).
• Recommendation Systems: collaborative filtering, matrix factorization.
• Production: packaging models in Spring Boot, REST API, Docker, monitoring, A/B testing.

Who is this book for?

The book is intended for:

• Java developers (Junior/Middle level) who want to transition to Data Science or add ML to their stack.
• Backend engineers building microservices who want to integrate machine learning models.
• Students and teachers studying machine learning in Java.
• Technical managers seeking to understand the possibilities of ML in the Java ecosystem.

What will you learn after reading?

• How to choose the right algorithm for a classification, regression, or clustering task.
• How to work with Deeplearning4j: build and train neural networks, load pre-trained models.
• How to process text in Java: tokenization, vectorization (Word2Vec, TF-IDF), sentiment analysis.
• How to deploy a model to production: create a REST service with Spring Boot, package in Docker, set up CI/CD.
• How to optimize performance: parallel processing, GPU acceleration, model quantization.
• How to test ML models: metrics (accuracy, precision, recall, F1), cross-validation.

Each chapter contains practical examples, ready-to-use code, and exercises for independent work. All examples are available in a GitHub repository. The book is written in accessible language, without excessive mathematics — the focus is on practice and intuitive understanding of algorithms.

"Machine Learning and Java" is a bridge between classical Java development and modern artificial intelligence. Start using ML in your projects today!