Mastering Java Machine Learning

Key FeaturesMaster the tools, frameworks and Machine Learning techniques in JavaBuild efficient predictive models and solutions to tackle real-world problemsYour one-stop guide to truly master Machine Learning using Java - the go-to language in data science todayBook DescriptionJava is used as a dominant language for most Data science areas, including Hadoop being created in Java. Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in the field of Data Science.This book aims to introduce you to an array of advanced techniques in machine learning including supervised and semi-supervised learning, clustering and anomaly detection, big data and stream machine learning. This book will also present special topics such as probabilistic graph modeling and evolutionary programming methods. Accompanying each chapter are illustrative examples of how to apply the newly learned techniques with the help of the best available tools for the Java Virtual Machine.On completion of this book you will have the knowledge and tools to build powerful predictive models to meet the challenge of Big Data problems.What you will learnDiscover key Java machine learning libraries and what kind of problems each are able to solveExplore powerful techniques in each major category of machine learning.Apply machine learning to fraud, anomaly, and outlier detectionExperiment with deep learning concepts, algorithms, and the toolbox for deep learningBuild high-performing, real-time, adaptive predictive models for stream based data learning.Get a deeper understanding of technologies leading towards a more powerful AI.About the AuthorDr. Uday KamathDr. Kamath is the Chief Data Scientist at BAE Systems Applied Intelligence. Dr. Kamath specializes in scalable machine learning and has spent more than 15 years in the domain of AML, fraud detection in financial and insurance domain, Cyber Security, and Bioinformatics, to name a few. Dr. Kamath is responsible for key products in the areas focusing on the behavioral, social-networking and Big Data machine learning aspects of analytics at BAE AI. Dr. Kamath received his Ph.D. at George Mason University, where his dissertation research focused on evolutionary machine learning for Big Data Sequence Mining. He has published extensively in statistics, machine learning, evolutionary computation, parallel computing and computational mathematics and biology journals, conferences and books. ChoppellaKrishna builds tools and client solutions in his role as Solutions Architect for Analytics at BAE Systems Applied Intelligence. He has been programming in Java for twenty years. His interests are data science, functional programming and distributed computing.

Author: Dr. Uday Kamath

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