Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

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.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Tools and usage


In this section, we will introduce two tools in Java that are very popular for probabilistic graph modeling.

OpenMarkov

OpenMarkov is a Java-based tool for PGMs and here is the description from www.openmarkov.org:

Note

OpenMarkov is a software tool for probabilistic graphical models (PGMs) developed by the Research Centre for Intelligent Decision-Support Systems of the UNED in Madrid, Spain.

It has been designed for: editing and evaluating several types of PGMs, such as Bayesian networks, influence diagrams, factored Markov models, and so on, learning Bayesian networks from data interactively, and cost-effectiveness analysis.

OpenMarkov is very good in performing interactive and automated learning from the data. It has capabilities to preprocess the data (discretization using frequency and value) and perform structure and parameter learning using a few search algorithms such as search-based Hill Climbing and score-based PC. OpenMarkov stores the models in a format known as pgmx...