Mastering Java Machine Learning

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning About This Book. Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects. More than 15 open source Java tools in a wide range of techniques with code and practical usage.. More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments all preparing the user for the practical real-world use of tools and data analysis. Who This Book Is For This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally intermediate-level data analysts and data scientists with experience in Java. Preferably you will have experience with the fundamentals of machine learning and now have a desire to explore the area further are up to grappling with the mathematical complexities of its algorithms and you wish to learn the complete ins and outs of practical machine learning. What You Will Learn. Master key Java machine learning libraries and what kind of problem each can solve with theory and practical guidance.. Explore powerful techniques in each major category of machine learning such as classification clustering anomaly detection graph modeling and text mining.. Apply machine learning to real-world data with methodologies processes applications and analysis.. Techniques and experiments developed around the latest specializations in machine learning such as deep learning stream data mining and active and semi-supervised learning.. Build high-performing real-time adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.. Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security Financial Crime Internet of Things social networking and so on. In Detail 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. Style and approach A practical guide to help you explore machine learning-and an array of Java-based tools and frameworks-with the help of practical examples and real-world use cases. About the Author Dr. 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. https:/www.linkedin.com/in/udaykamath http:/digilib.gmu.edu/xmlui/bitstream/handle/1920/8909/Kamath_gmu_0883E_10523.pdf.sequence=1&isAllowed=y http:/journals.plos.org/plosone/article.id=10.1371/journal.pone.0099982 http:/comments.gmane.org/gmane.comp.ai.weka/35040 Krishna 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.