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

Summary


PGMs capture domain knowledge as relationships between variables and represent joint probabilities. They are used in a range of applications.

Probability maps an event to a real value between 0 and 1 and can be interpreted as a measure of the frequency of occurrence (frequentist view) or as a degree of belief in that occurrence (Bayesian view). Concepts of random variables, conditional probabilities, Bayes' theorem, chain rule, marginal and conditional independence and factors form the foundations to understanding PGMs. MAP and Marginal Map queries are ways to ask questions about the variables and relationships in the graph.

The structure of graphs and their properties such as paths, trails, cycles, sub-graphs, and cliques are vital to the understanding of Bayesian networks. Representation, Inference, and Learning form the core elements of networks that help us capture, extract, and make predictions using these methods. From the representation of graphs, we can reason about the flow...