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

Real-world case study


Here we present a case study that illustrates how to apply clustering and outlier techniques described in this chapter in the real world, using open-source Java frameworks and a well-known image dataset.

Tools and software

We will now introduce two new tools that were used in the experiments for this chapter: SMILE and Elki. SMILE features a Java API that was used to illustrate feature reduction using PCA, Random Projection, and IsoMap. Subsequently, the graphical interface of Elki was used to perform unsupervised learning—specifically, clustering and outlier detection. Elki comes with a rich set of algorithms for cluster analysis and outlier detection including a large number of model evaluators to choose from.

Note

Find out more about SMILE at: http://haifengl.github.io/smile/ and to learn more about Elki, visit http://elki.dbs.ifi.lmu.de/.

Business problem

Character-recognition is a problem that occurs in many business areas, for example, the translation of medical reports...