Posted by **insetes** at Oct. 17, 2017

2014 | 426 Pages | ISBN: 1461471370 | PDF | 11 MB

Posted by **ChrisRedfield** at Jan. 22, 2017

Published: 2013-08-12 | ISBN: 1461471370 | PDF | 426 pages | 10.71 MB

Posted by **AlenMiler** at Oct. 21, 2015

English | 18 Sept. 2015 | ISBN: 1634620968 | 282 Pages | True AZW3 (Kindle)/(EPUB/PDF conv) | 15.44 MB

A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation.

Posted by **enmoys** at Nov. 1, 2014

2014 | 426 Pages | ISBN: 1461471370 | PDF | 11 MB

Posted by **advisors** at Sept. 3, 2014

2014 | 426 Pages | ISBN: 1461471370 | PDF | 11 MB

Posted by **bookwyrm** at June 29, 2014

2014 | 426 Pages | ISBN: 1461471370 | PDF | 11 MB

Posted by **insetes** at Jan. 8, 2018

2015 | 356 Pages | ISBN: 1484203747 | PDF | 11 MB

Posted by **AvaxGenius** at Dec. 27, 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 **naag** at Dec. 14, 2017

MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 39M | 1.38 GB

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.