Machine Learning For Predictive Data Analytics

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies By John D. Kelleher
2015 | 624 Pages | ISBN: 0262029448 | EPUB | 7 MB
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher
English | July 24, 2015 | ISBN: 0262029448 | 624 Pages | AZW3/PDF (conv) | 24 MB

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification.

Machine Learning for Adaptive Many-Core Machines - A Practical Approach  eBooks & eLearning

Posted by naag at Jan. 7, 2016
Machine Learning for Adaptive Many-Core Machines - A Practical Approach

Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data) by Noel Lopes and Bernardete Ribeiro
English | 2014 | ISBN: 3319069373, 331906939X | 241 pages | Epub (conv) | 7.77 MB

Machine Learning for Adaptive Many-Core Machines - A Practical Approach  eBooks & eLearning

Posted by interes at Nov. 15, 2014
Machine Learning for Adaptive Many-Core Machines - A Practical Approach

Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data) by Noel Lopes and Bernardete Ribeiro
English | 2014 | ISBN: 3319069373, 331906939X | 241 pages | PDF | 18 MB

The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry.

Machine Learning for Data Science  eBooks & eLearning

Posted by naag at Jan. 10, 2017
Machine Learning for Data Science

Machine Learning for Data Science
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 32 Hours | Lec: 38 | 1.98 GB
Genre: eLearning | Language: English

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
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 for Health Informatics: State-of-the-Art and Future Challenges  eBooks & eLearning

Posted by hill0 at Dec. 11, 2016
Machine Learning for Health Informatics: State-of-the-Art and Future Challenges

Machine Learning for Health Informatics: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science) by Andreas Holzinger
English | 4 Jan. 2017 | ISBN: 3319504770 | 504 Pages | EPUB | 4.74 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.

An Introduction to Machine Learning with Web Data [repost]  eBooks & eLearning

Posted by FenixN at Nov. 28, 2016
An Introduction to Machine Learning with Web Data [repost]

An Introduction to Machine Learning with Web Data
HDRips | MP4/AVC, ~1200 kb/s | 1280x720 | Duration: 02:43:22 | English: AAC, 128 kb/s (2 ch) | 1.43 GB
Genre: Development / Programming

Once you've accumulated a pile of data through your web application, what do you do with it? In this insightful video course, bit.ly lead scientist Hilary Mason shows you how to solve data analysis problems using basic machine learning techniques and frameworks. You'll follow several examples through the entire process—from obtaining, cleaning, and exploring data to building a model and interpreting the results.
Machine Learning for Multimodal Interaction: 5th International Workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2

Machine Learning for Multimodal Interaction: 5th International Workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008, Proceedings
English | October 7, 2008 | ISBN: 3540858520 | 375 Pages | PDF | 43 MB

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2008, held in Utrecht, The Netherlands, in September 2008. The 12 revised full papers and 15 revised poster papers presented together with 5 papers of a special session on user requirements and evaluation of multimodal meeting browsers/assistants were carefully reviewed and selected from 47 submissions.
Machine Learning for Multimodal Interaction: 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Rev

Machine Learning for Multimodal Interaction: 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers
English | April 10, 2008 | ISBN: 3540781544 | 317 Pages | PDF | 22 MB

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2007, held in Brno, Czech Republic, in June 2007. The 25 revised full papers presented together with 1 invited paper were carefully selected during two rounds of reviewing and revision from 60 workshop presentations.