Machine Learning with R by Abhijit Ghatak (English) Hardcover Book

Description: Machine Learning with R by Abhijit Ghatak This book helps readers understand the mathematics of machine learning, and apply them in different situations. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and its applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readerscan modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning. Back Cover This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and its applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning. Author Biography Abhijit Ghatak is a Data Scientist and holds an ME in Engineering and MS in Data Science from Stevens Institute of Technology, USA. He started his career as a submarine engineer officer in the Indian Navy and worked on multiple data-intensive projects involving submarine operations and construction. He has worked in academia, technology companies and as a research scientist in the area of Internet of Things (IoT) and pattern recognition for the European Union (EU). He has published in the areas of engineering and machine learning and is presently a consultant in the area of pattern recognition and data analytics. His areas of research include IoT, stream analytics and design of deep learning systems. Table of Contents Chapter 1. Linear Algebra, Numerical Optimization and its Applications in Machine Learning.- Chapter 2. Probability and Distributions.- Chapter 3.Introduction to Machine Learning.- Chapter 4. Regression.- Chapter 5. Classification.- Chapter 6. Clustering. Feature Help readers understand the mathematical interpretation of learning algorithms Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc Description for Sales People Help readers understand the mathematical interpretation of learning algorithms Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc Details ISBN9811068070 Author Abhijit Ghatak Publisher Springer Verlag, Singapore Year 2017 Edition 1st ISBN-10 9811068070 ISBN-13 9789811068072 Format Hardcover Imprint Springer Verlag, Singapore Place of Publication Singapore Country of Publication Singapore Pages 210 Illustrations 56 Illustrations, black and white; XIX, 210 p. 56 illus. DEWEY 005.11 Publication Date 2017-12-07 Language English DOI 10.1007/978-981-10-6808-9 UK Release Date 2017-12-07 Edited by Francois Raulin Birth 1974 Affiliation European University Viadrina, Germany Position journalist Qualifications S. J. Edition Description 1st ed. 2017 Alternative 9789811349508 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:130686816;

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