Machine Learning and Java: A Practical Guide

The book "Machine Learning and Java" is a practical guide for developers who want to master machine learning (ML) in Java. Unlike popular Python solutions, Java offers high performance, strong typing, and extensive integration capabilities for enterprise systems. This book will help you bridge the gap between ML theory and real-world Java development.

What this book covers

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

• ML Basics: linear regression, logistic regression, decision trees, random forest, support vector machines (SVM).
• Deep Learning: neural networks, convolutional networks (CNN) for images, recurrent networks (RNN) for sequences.
• Java Libraries: Deeplearning4j (DL4J), Weka, Smile, Apache Spark MLlib.
• Data Processing: loading, cleaning, transforming data using Java Streams and Apache Commons Math.
• Integration: deploying models in Spring Boot, creating REST APIs for predictions, working with databases.
• Production Scenarios: model monitoring, A/B testing, performance optimization.

Who this book is for

• Java developers (Junior, Middle, Senior) looking to add ML to their toolkit.
• Data Sc