Mastering Java Machine Learning : A Java developer's guide to implementing machine learning and big data architectures
Language: English
Published by Packt Publishing, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 123.81
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Become an advanced practitioner with this progressive set of master classes on application-oriented machine learningKey FeaturesComprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspectsMore 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.Book DescriptionJava 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.What you will learnMaster 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.Who this book is forThis 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.…
Seller Inventory # 9781785880513
- Title
- Mastering Java Machine Learning : A Java developer's guide to implementing machine learning and big data architectures
- Author
- Krishna Choppella
- Publisher
- Packt Publishing
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1785880519
- ISBN 13
- 9781785880513
- Item weight
- 1,025 grams
- Dimensions
- 235x191x30 mm
Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning
Key Features
- 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.
Book Description
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.
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.
Table of Contents
- Revisiting Machine Learning Basics
- Practical Approach in Real-World Supervised Learning
- Advanced Topics in Clustering and Anomaly Detection
- Methodology for Real-world Semi-Supervised Learning
- Real-time Stream Machine Learning
- Probabilistic Graph Modelling
- Deep Learning
- Probabilistic Graph Modeling and Graph Data Learning
- Related Topics in Machine Learning
- Linear Algebra
- Probability
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Uday Kamath is the chief data scientist at BAE Systems Applied Intelligence. He specializes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in financial crime, cyber security, and bioinformatics, to name a few. Dr. Kamath is responsible for key products in areas focusing on the behavioral, social networking and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining.
Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.
"About the title" may belong to another edition of this title.
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