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

About the Reviewers

Samir Sahli was awarded a BSc degree in applied mathematics and information sciences from the University of Nice Sophia-Antipolis, France, in 2004. He received MSc and PhD degrees in physics (specializing in optics/photonics/image science) from University Laval, Quebec, Canada, in 2008 and 2013, respectively. During his graduate studies, he worked with Defence Research and Development Canada (DRDC) on the automatic detection and recognition of targets in aerial imagery, especially in the context of uncontrolled environment and sub-optimal acquisition conditions. He has worked since 2009 as a consultant for several companies based in Europe and North America specializing in the area of Intelligence, Surveillance, and Reconnaissance (ISR) and in remote sensing.

Dr. Sahli joined McMaster Biophotonics in 2013 as a postdoctoral fellow. His research was in the field of optics, image processing, and machine learning. He was involved in several projects, such as the development of a novel generation of gastrointestinal tract imaging device, hyperspectral imaging of skin erythema for individualized radiotherapy treatment, and automatic detection of the precancerous Barrett's esophageal cell using fluorescence lifetime imaging microscopy and multiphoton microscopy.

Dr. Sahli joined BAE Systems Applied Intelligence in 2015. He has since worked as a data scientist to develop analytics models to detect complex fraud patterns and money laundering schemes for insurance, banking, and governmental clients using machine learning, statistics, and social network analysis tools.

Prashant Verma started his IT career in 2011 as a Java developer in Ericsson, working in the telecom domain. After a couple of years of Java EE experience, he moved into the big data domain and has worked on almost all of the popular big data technologies such as Hadoop, Spark, Flume, Mongo, Cassandra, and so on. He has also played with Scala. Currently, he works with QA Infotech as a lead data engineer, working on solving e-learning problems with analytics and machine learning.

Prashant has worked for many companies, such as Ericsson and QA Infotech, with domain knowledge of telecom and e-learning. He has also worked as a freelance consultant in his free time.