Machine Learning in Java - Second Edition

"Machine Learning in Java" offers a comprehensive guide to building practical machine learning applications using Java. This book covers a range of topics from classification and regression to text analysis, providing clear and applicable examples to help you understand and implement machine learning techniques effectively. You'll gain hands-on experience that will empower you to tackle real-world data challenges.

What this Book will help me do

• Understand and apply key machine learning concepts like classification, regression, and clustering in Java.
• Gain proficiency with Java libraries such as WEKA, MALLET, and Deeplearning4j for machine learning tasks.
• Develop actionable solutions such as scalable recommendation systems and fraud detection tools.
• Implement text and image recognition models with a focus on practical application in real-world scenarios.
• Learn the essentials of preparing data, selecting methods, and evaluating machine learning models.

Author(s)

Ashish Bhatia is a seasoned data scientist with extensive experience in applying machine learning solutions to solve practical problems. Bostjan Kaluza holds a PhD in artificial intelligence and is recognized for his hands-on approach to leveraging machine learning in software development. Together, they bring a blend of theoretical knowledge and practical insight to help readers navigate the intricacies of implementing machine learning in Java.

Who is it for?

This book is ideal for Java developers and software engineers looking to transition into machine learning, or data practitioners seeking to apply their knowledge within the Java ecosystem. It is suitable for readers who have foundational programming skills in Java and an interest in data science concepts. By following this guide, you will gain practical experience and understanding to confidently develop machine learning applications in Java.