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

Kamath, Dr. Uday; Choppella, Krishna

Softcover

ISBN 10: 1785880519 ISBN 13: 9781785880513

Publisher: Packt Publishing, 2017

Synopsis

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

About this title

From the Author

"One of the best Machine Learning books of all time" - BookAuthorityThe book is more for the breadth and covers many topics that are useful today in machine learning. It assumes very basic knowledge of linear algebra, probability, and statistics (has an appendix to review important topics). It details important topics in machine learning that are made practical with help of case studies using a Java library or a tool and process from data acquisition to model validation and analysis. Many topics such as "active learning", "semi-supervised learning", "probabilistic graph mining" etc. which are so essential in real-world with limited annotations or large subject matter expertise inputs but are not that well covered by the introduction, practical tools, and processes in our opinion. Hence many such topics are covered along with standard topics such as classification, clustering, text-mining, deep learning, big data machine learning, etc. Enjoy!

About the Author

Dr. Uday Kamath is the chief data scientist at BAE Systems Applied Intelligence. He specializes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in financial crime, cyber security, and bioinformatics, to name a few. Dr. Kamath is responsible for key products in areas focusing on the behavioral, social networking and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining.

Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.

Edition details

• Publisher: Packt Publishing
• Publication date: 2017
• ISBN 10: 1785880519
• ISBN 13: 9781785880513
• Binding: Paperback
• Language: English
• Number of pages: 556