Posted by **naag** at July 26, 2017

English | 2017 | ASIN: B0743826F1 | 45 pages | AZW3 | 0.2 Mb

Posted by **bookwarrior** at Dec. 19, 2015

2012 | 324 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Posted by **bookwyrm** at March 12, 2012

Publisher: O'R||eil||ly Me||dia 2012 | 322 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Posted by **AlenMiler** at Dec. 10, 2017

English | 1 Dec. 2017 | ISBN: 1973443503 | ASIN: B077WFS87Z | 246 Pages | AZW3 | 2.24 MB

Posted by **exLib** at Dec. 8, 2017

ITexLi | 2017 | ISBN: 9533070331 9789533070339 | 446 pages | PDF | 24 MB

This book presents today’s state and development tendencies of machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning

Posted by **naag** at Dec. 7, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 398 MB

Posted by **AvaxGenius** at Dec. 3, 2017

English | EPUB | 2015 | 291 Pages | ISBN : 3319200097 | 2.66 MB

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines.

Posted by **AvaxGenius** at Dec. 3, 2017

English | EPUB | 2017 | 348 Pages | ISBN : 3319639129 | 3.13 MB

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines.

Posted by **naag** at Dec. 3, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 48M | 2.03 GB

Posted by **AvaxGenius** at Nov. 25, 2017

English | PDF,EPUB | 2017 | 224 Pages | ISBN : 9811068070 | 7.02 MB

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.