Machine Learning and Java: From Basics to Industrial Solutions

The book "Machine Learning and Java" is a unique practical guide that bridges the gap between machine learning theory and real-world Java development. While most ML materials are oriented towards Python, this book proves that Java is a powerful and in-demand tool for building production artificial intelligence systems.

What is this book about?

You will journey from basic concepts to creating complete ML solutions. The book covers:

• Fundamentals of Machine Learning — regression, classification, clustering, decision trees, and ensembles. All algorithms are implemented in Java "from scratch" and using libraries.
• Working with Data — loading, cleaning, transformation, and visualization. Using Apache Commons Math, Smile, ND4J, and Java Stream API.
• Deep Learning — building neural networks with Deeplearning4j. Practice: image recognition, text processing, time series.
• Industrial Deployment — how to wrap a model in a REST API (Spring Boot), save and load trained models, integrate ML into microservices.
• Performance — optimizing computations, working with multithreading, using GPU via CUDA.

Who is this book for?

• Java developers who want to add ML to their stack.
• Data Science professionals transitioning to Java for production.
• Students and teachers studying the practical application of machine learning.
• Software architects who need ready-made patterns for ML systems.

What will you learn?

• How to write a K-Nearest Neighbors or Logistic Regression algorithm in pure Java.
• How to use the Smile, Tribuo, Weka, and DL4J libraries for rapid prototyping.
• How to turn a Jupyter notebook into a Java service with an API.
• How to build recommendation systems, anomaly detectors, and predictive models.
• How to test and debug ML models in a Java environment.

The book contains over 50 ready-to-run code examples. All examples are accompanied by explanations and visualizations. You will learn not just to run models, but to create reliable, scalable solutions in Java. This is an ideal choice for those who want to stay within the Java ecosystem while keeping up with modern AI trends.

Note: A basic knowledge of Java (OOP, collections, streams) is sufficient for reading. Deep knowledge of mathematics is not required — all concepts are explained accessibly from a practical perspective.