Posted by **naag** at Jan. 10, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 32 Hours | Lec: 38 | 1.98 GB

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

Posted by **exLib** at Nov. 21, 2011

Sрringеr Science+Business Media | 2006 | ISBN: 0387310732 0387310738 | 101 pages | PDF | 1 MB

Example solutions for a subset of the exercises are available from the book. This issue is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. A volume deals with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets.

Posted by **exLib** at Nov. 21, 2011

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.

Posted by **foosaa** at July 22, 2009

Springer | 2007 | ISBN: 0387310738 | English | 738 Pages | PDF | 9.5 MB

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Posted by **DZ123** at Jan. 16, 2017

English | 2012 | ISBN: 026201825X | PDF | pages: 427 | 2.7 mb

Posted by **ksveta6** at Jan. 12, 2017

2016 | ISBN: 1118745671 | English | 320 pages | PDF/EPUB | 4 MB/34 MB

Posted by **naag** at Jan. 11, 2017

English | 10 Jan. 2017 | ISBN: 1484223330 | 592 Pages | Epub | 8.33 MB

This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.

Posted by **hill0** at Jan. 8, 2017

English | 10 Dec. 2016 | ISBN: 3319504770 | 504 Pages | PDF | 27 MB

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.

Posted by **DZ123** at Jan. 8, 2017

English | 2014 | ISBN: 1783988363 | PDF | pages: 238 | 3.6 mb

Posted by **hill0** at Jan. 6, 2017

English | 10 Jan. 2017 | ISBN: 1484223330 | 592 Pages | PDF | 11.47 MB

This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.