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

Graph concepts


Next, we will briefly revisit the concepts from graph theory and some of the definitions that we will use in this chapter.

Graph structure and properties

A graph is defined as a data structure containing nodes and edges connecting these nodes. In the context of this chapter, the random variables are represented as nodes, and edges show connections between the random variables.

Formally, if X = {X1, X2,….Xk} where X1, X2,….Xk are random variables representing the nodes, then there can either be a directed edge belonging to the set e, for example, between the nodes given by or an undirected edge , and the graph is defined as a data structure . A graph is said to be a directed graph when every edge in the set e between nodes from set X is directed and similarly an undirected graph is one where every edge between the nodes is undirected as shown in Figure 1. Also, if there is a graph that has both directed and undirected edges, the notation of represents an edge that may be...