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Machine Learning with R

Authors: Ghatak, Abhijit

  • ​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
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eBook $54.99
price for USA (gross)
  • ISBN 978-981-10-6808-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $69.99
price for USA
  • ISBN 978-981-10-6807-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

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 it’s 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.

 

About the authors

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 (6 chapters)

Buy this book

eBook $54.99
price for USA (gross)
  • ISBN 978-981-10-6808-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $69.99
price for USA
  • ISBN 978-981-10-6807-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning with R
Authors
Copyright
2017
Publisher
Springer Singapore
Copyright Holder
Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-10-6808-9
DOI
10.1007/978-981-10-6808-9
Hardcover ISBN
978-981-10-6807-2
Edition Number
1
Number of Pages
XIX, 210
Number of Illustrations and Tables
56 b/w illustrations
Topics