Machine Learning By Example

R Machine Learning By Example  eBooks & eLearning

Posted by AlenMiler at April 4, 2016
R Machine Learning By Example

R Machine Learning By Example by Raghav Bali
English | May 5, 2016 | ISBN: 1784390844 | 340 Pages | AZW3/MOBI/EPUB/PDF (conv) | 41.62 MB

If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you.

R: Unleash Machine Learning Techniques  eBooks & eLearning

Posted by Grev27 at Oct. 27, 2016
R: Unleash Machine Learning Techniques

Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister, "R: Unleash Machine Learning Techniques"
English | ISBN-13: 9781787127340, ASIN: B01MQ4M4VO | 2016 | PDF/EPUB/MOBI | 1123 pages | 26 MB/32 MB/49 MB

Scala for Machine Learning by Patrick R. Nicolas [Repost]  eBooks & eLearning

Posted by tanas.olesya at June 1, 2016
Scala for Machine Learning by Patrick R. Nicolas [Repost]

Scala for Machine Learning by Patrick R. Nicolas
English | 2014 | ISBN: 1783558741 | 520 Pages | PDF | 5 MB

The book begins with an introduction to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.
Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand[Repost]

Principles of Data Mining (Adaptive Computation and Machine Learning) (Scan) by David J. Hand
English | Aug 1, 2001 | ISBN: 026208290X | 546 Pages | PDF | 30 MB

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines.

Machine Learning by Tom M. Mitchell [Repost]  eBooks & eLearning

Posted by tanas.olesya at Oct. 8, 2014
Machine Learning by Tom M. Mitchell [Repost]

Machine Learning by Tom M. Mitchell
McGraw-Hill Science/Engineering/Math; 1 edition | March 1, 1997 | English | ISBN: 0070428077 | 421 pages | PDF | 37 MB

This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning–including probability and statistics, artificial intelligence, and neural networks–unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
Principles of Data Mining (Adaptive Computation and Machine Learning) by  David J. Hand

Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth
Publisher: The MIT Press (August 1, 2001) | ISBN-10: 026208290X | PDF | 30,6 Mb | 425 pages

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

"Pattern Recognition and Machine Learning" by Christopher M. Bishop  eBooks & eLearning

Posted by exLib at Nov. 21, 2011
"Pattern Recognition and Machine Learning" by Christopher M. Bishop

"Pattern Recognition and Machine Learning" by Christopher M. Bishop
Information Science and Statistics
Sрringеr Science+Business Media | 2006 | ISBN: 0387310738 9780387310732 | 761 pages | PDF | 5 MB

This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

A First Course in Machine Learning, 2nd Edition  eBooks & eLearning

Posted by ksveta6 at Feb. 21, 2017
A First Course in Machine Learning, 2nd Edition

A First Course in Machine Learning, Second Edition (Machine Learning & Pattern Recognition) by Simon Rogers, Mark Girolami
2016 | ISBN: 1498738486 | English | 427 pages | PDF | 161 MB

MATLAB Machine Learning [repost]  eBooks & eLearning

Posted by naag at Jan. 28, 2017
MATLAB Machine Learning [repost]

MATLAB Machine Learning By Stephanie Thomas, Michael Paluszek
English | EPUB | 326 Pages | 2017 | ISBN : 1484222490 | 5.17 MB

MATLAB Machine Learning  eBooks & eLearning

Posted by hill0 at Jan. 20, 2017
MATLAB Machine Learning

MATLAB Machine Learning by Michael Paluszek
English | 31 Jan. 2017 | ISBN: 1484222490 | 348 Pages | PDF | 9.87 MB

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning.