Machine Learning a Probabilistic Perpective

Machine Learning: A Probabilistic Perspective (Repost)  eBooks & eLearning

Posted by nebulae at Nov. 19, 2015
Machine Learning: A Probabilistic Perspective (Repost)

Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective"
English | ISBN: 0262018020 | 2012 | PDF | 1104 pages | 25,7 MB

Machine Learning: A Probabilistic Perspective (Repost)  eBooks & eLearning

Posted by Grev27 at Sept. 6, 2013
Machine Learning: A Probabilistic Perspective (Repost)

Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective"
English | ISBN: 0262018020 | 2012 | PDF | 1104 pages | 25,7 MB

Machine Learning: A Probabilistic Perspective  eBooks & eLearning

Posted by enmoys at Dec. 8, 2012
Machine Learning: A Probabilistic Perspective

Machine Learning: A Probabilistic Perspective By Kevin P. Murphy
2012 | 1104 Pages | ISBN: 0262018020 | PDF | 26 MB
Reverse Hypothesis Machine Learning: A Practitioner's Perspective (Intelligent Systems Reference Library)

Reverse Hypothesis Machine Learning: A Practitioner's Perspective (Intelligent Systems Reference Library) by Parag Kulkarni
English | 2017 | ISBN: 3319553119 | 138 Pages | PDF | 3.23 MB

Reverse Hypothesis Machine Learning: A Practitioner's Perspective  eBooks & eLearning

Posted by AvaxGenius at March 30, 2017
Reverse Hypothesis Machine Learning: A Practitioner's Perspective

Reverse Hypothesis Machine Learning: A Practitioner's Perspective By Parag Kulkarni
English | PDF | 2017 | 150 Pages | ISBN : 3319553119 | 4.53 MB

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming.

Combinatorial Machine Learning: A Rough Set Approach  eBooks & eLearning

Posted by alt_f4 at Sept. 27, 2015
Combinatorial Machine Learning: A Rough Set Approach

Combinatorial Machine Learning: A Rough Set Approach (Studies in Computational Intelligence) by Mikhail Moshkov
English | June 29, 2011 | ISBN: 3642209947 | 186 Pages | PDF | 2 MB

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection.

Thoughtful Machine Learning: A Test-Driven Approach  eBooks & eLearning

Posted by ksveta6 at Jan. 5, 2015
Thoughtful Machine Learning: A Test-Driven Approach

Thoughtful Machine Learning: A Test-Driven Approach by Matthew Kirk
2014 | ISBN: 1449374069 | English | 234 pages | PDF + EPUB | 6 MB + 19 MB

Multi-Agent Machine Learning: A Reinforcement Approach  eBooks & eLearning

Posted by Bayron at Oct. 19, 2014
Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Approach by H. M. Schwartz
English | 2014 | ISBN: 111836208X | 256 pages | EPUB | 13 MB

Practical Machine Learning  eBooks & eLearning

Posted by AvaxGenius at May 21, 2017
Practical Machine Learning

Practical Machine Learning By Sunila Gollapudi
English | EPUB | 20116 | 468 Pages | ISBN : 178439968X | 23 MB

Finding something meaningful in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behavior of datasets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, which is immensely valuable to the growth and development of business.

Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning  eBooks & eLearning

Posted by andr1078 at May 17, 2017
Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning

Mr. Alboukadel Kassambara "Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning "
Publisher: CreateSpace Independent Publishing Platform | English | 2017 | ISBN: 1542462703 | 188 pages | PDF | 5 MB

This book provides practical guide to cluster analysis, elegant visualization and interpretation.