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

Case study in stream learning


The case study in this chapter consists of several experiments that illustrate different methods of stream-based machine learning. A well-studied dataset was chosen as the stream data source and supervised tree based methods such as Naïve Bayes, Hoeffding Tree, as well as ensemble methods, were used. Among unsupervised methods, clustering algorithms used include k-Means, DBSCAN, CluStream, and CluTree. Outlier detection techniques include MCOD and SimpleCOD, among others. We also show results from classification experiments that demonstrate handling concept drift. The ADWIN algorithm for calculating statistics in a sliding window, as described earlier in this chapter, is employed in several algorithms used in the classification experiments.

Tools and software

One of the most popular and arguably the most comprehensive Java-based frameworks for data stream mining is the open source Massive Online Analysis (MOA) software created by the University of Waikato. The...