Algorithms Machine Learning

Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms

Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms
English | 2017 | ASIN: B0743826F1 | 45 pages | AZW3 | 0.2 Mb

Machine Learning for Hackers (Repost)  eBooks & eLearning

Posted by bookwarrior at Dec. 19, 2015
Machine Learning for Hackers (Repost)

Machine Learning for Hackers By Drew Conway, John Myles White
2012 | 324 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Machine Learning for Hackers  eBooks & eLearning

Posted by bookwyrm at March 12, 2012
Machine Learning for Hackers

Machine Learning for Hackers By Drew Conway, John Myles White
Publisher: O'R||eil||ly Me||dia 2012 | 322 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Practical Machine Learning with R and Python: Machine Learning in Stereo  eBooks & eLearning

Posted by AlenMiler at Dec. 10, 2017
Practical Machine Learning with R and Python: Machine Learning in Stereo

Practical Machine Learning with R and Python: Machine Learning in Stereo by Tinniam V Ganesh
English | 1 Dec. 2017 | ISBN: 1973443503 | ASIN: B077WFS87Z | 246 Pages | AZW3 | 2.24 MB

"Machine Learning" ed. by Yagang Zhang  eBooks & eLearning

Posted by exLib at Dec. 8, 2017
"Machine Learning" ed. by Yagang Zhang

"Machine Learning" ed. by Yagang Zhang
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

Extending Machine Learning Algorithms  eBooks & eLearning

Posted by naag at Dec. 7, 2017
Extending Machine Learning Algorithms

Extending Machine Learning Algorithms
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 398 MB
Genre: eLearning | Language: English

An Introduction to Machine Learning  eBooks & eLearning

Posted by AvaxGenius at Dec. 3, 2017
An Introduction to Machine Learning

An Introduction to Machine Learning By Miroslav Kubat
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.

An Introduction to Machine Learning, Second Edition  eBooks & eLearning

Posted by AvaxGenius at Dec. 3, 2017
An Introduction to Machine Learning, Second Edition

An Introduction to Machine Learning, Second Edition By Miroslav Kubat
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.

Getting Started with MATLAB Machine Learning  eBooks & eLearning

Posted by naag at Dec. 3, 2017
Getting Started with MATLAB Machine Learning

Getting Started with MATLAB Machine Learning
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 48M | 2.03 GB
Genre: eLearning | Language: English

Machine Learning with R  eBooks & eLearning

Posted by AvaxGenius at Nov. 25, 2017
Machine Learning with R

Machine Learning with R By Abhijit Ghatak
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.