Machine Learning Approach

Intrusion Detection: A Machine Learning Approach, Volume 3  eBooks & eLearning

Posted by interes at Dec. 12, 2015
Intrusion Detection: A Machine Learning Approach, Volume 3

Intrusion Detection: A Machine Learning Approach, Volume 3 (Series in Electrical and Computer Engineering) by Zhenwei Yu and Jeffrey J P Tsai
English | 2011 | ISBN: 1848164475 | 184 pages | PDF | 1 MB

Rule Based Systems for Big Data: A Machine Learning Approach (Repost)  eBooks & eLearning

Posted by roxul at Oct. 1, 2015
Rule Based Systems for Big Data: A Machine Learning Approach (Repost)

Han Liu, Alexander Gegov, Mihaela Cocea, "Rule Based Systems for Big Data: A Machine Learning Approach"
English | 2015 | ISBN-10: 3319236954 | 121 pages | pdf | 2.8 MB
Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning Series) [Repost]

Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning Series) by Pierre Baldi
English | 10 Aug. 2001 | ISBN: 026202506X | 400 Pages | PDF | 3.29 MB

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules.

Pierre Baldi, Bioinformatics: The Machine Learning Approach  eBooks & eLearning

Posted by Direktor69 at July 18, 2013
Pierre Baldi, Bioinformatics: The Machine Learning Approach

Pierre Baldi, Bioinformatics: The Machine Learning Approach
ISBN: 026202506X | edition 2001 | PDF | 477 pages | 6 mb

Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology

Bioinformatics: The Machine Learning Approach, 2nd Edition  eBooks & eLearning

Posted by SweetStroke at Aug. 16, 2006
Bioinformatics: The Machine Learning Approach, 2nd Edition

Pierre Baldi, Søren Brunak, «Bioinformatics: The Machine Learning Approach, 2nd Edition»
The MIT Press | ISBN 026202506X | 2001 Year | PDF | 5,94 Mb | 400 Pages

"Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology."
Handbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques (2 Volumes) (repost)

Emilio Soria Olivas, Jose David Martin Guerrero, Marcelino Martinez Sober, and Jose Rafael Magdalena Benedito, "Handbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques (2 Volumes)"
Information Science Reference | 2009 | ISBN: 1605667668 | 834 pages | PDF | 11,9 MB

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

Posted by AlenMiler at Jan. 19, 2017
Thoughtful Machine Learning with Python: A Test-Driven Approach

Thoughtful Machine Learning with Python: A Test-Driven Approach by Matthew Kirk
English | 25 Aug. 2016 | ISBN: 1491924136 | 250 Pages | AZW3/MOBI/EPUB/PDF (conv) | 16.77 MB

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (Repost)  eBooks & eLearning

Posted by nebulae at Jan. 17, 2017
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (Repost)

Duriez, Thomas, Brunton, Steven L., Noack, Bernd R., "Machine Learning Control – Taming Nonlinear Dynamics and Turbulence"
English | 2016 | ISBN-10: 331940623X | 211 pages | pdf | 9 MB
Machine Learning for Health Informatics: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science)

Machine Learning for Health Informatics: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science) by Andreas Holzinger
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.

Machine Learning Using R  eBooks & eLearning

Posted by hill0 at Jan. 6, 2017
Machine Learning Using R

Machine Learning Using R by Karthik Ramasubramanian
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.