Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning

Key Features

• Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects
• More than 15 open source Java tools in a wide range of techniques, with code and practical usage.
• More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis.

Book Description

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science.

This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today.

On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.

What you will learn

• Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.
• Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.
• Apply machine learning to real-world data with methodologies, processes, applications, and analysis.
• Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning.
• Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.
• Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on.

Table of Contents

1. Revisiting Machine Learning Basics
2. Practical Approach in Real-World Supervised Learning
3. Advanced Topics in Clustering and Anomaly Detection
4. Methodology for Real-world Semi-Supervised Learning
5. Real-time Stream Machine Learning
6. Probabilistic Graph Modelling
7. Deep Learning
8. Probabilistic Graph Modeling and Graph Data Learning
9. Related Topics in Machine Learning
10. Linear Algebra
11. Probability