Big Data et Machine Learning Manuel du Data Scientist

Big Data et machine learning : Manuel du data scientist  eBooks & eLearning

Posted by ernaniaroldo at April 11, 2016
Big Data et machine learning : Manuel du data scientist

Big Data et machine learning : Manuel du data scientist
Dunod (18 février 2015) | ISBN: 2100720740 | Français | PDF | 240 pages | 79 MB

Ethical Reasoning in Big Data: An Exploratory Analysis  eBooks & eLearning

Posted by Underaglassmoon at May 3, 2016
Ethical Reasoning in Big Data: An Exploratory Analysis

Ethical Reasoning in Big Data: An Exploratory Analysis
Springer | Computer Science | May 24, 2016 | ISBN-10: 3319284207 | 192 pages | pdf | 2.9 mb

Editors: Collmann, Jeff, Matei, Sorin Adam (Eds.)
Defines and explains the main types of ethical dilemmas in handling big data
Proposes a set of principles for developing sound ethical handling of big data
Discusses methods for individual and organizational ethical reasoning in big data contexts
Presents methods for teaching ethical reasoning to big data researchers

Coursera - Machine Learning (2015)  

Posted by house23 at Feb. 17, 2016
Coursera - Machine Learning (2015)

Coursera - Machine Learning (2015)
MP4 | AVC 32kbps | English | 960x540 | 15fps | 19h 53mins | AAC stereo 128kbps | 1.52 GB
Genre: Video Training

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics (repost)

Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, ‘BI’ Implementations and Analytics (The Expert's Voice) by Soumendra Mohanty, Madhu Jagadeesh and Harsha Srivatsa
English | ISBN: 1430248726 | 2013 | 320 pages | EPUB | 6,3 MB

Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?
Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data (repost)

Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data by Peter Zadrozny and Raghu Kodali
English | ISBN: 143025761X | 2013 | PDF | 376 pages | 17 MB

Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins).
A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning

Antonio Gulli, "A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning"
English | ISBN: 1517216710 | 2015 | 84 pages | EPUB | 1 MB

Machine Intelligence and Big Data in Industry  eBooks & eLearning

Posted by Underaglassmoon at April 26, 2016
Machine Intelligence and Big Data in Industry

Machine Intelligence and Big Data in Industry
Springer | Studies in Big Data | March 25 2016 | ISBN-10: 3319303147 | 236 pages | pdf | 6.63 mb

Editors: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (Eds.)
Presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of industryExtended versions of presentations delivered at the Industrial Session of the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015
Addresses real world problems and shows innovative solutions for them
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R by Daniel D. Gutierrez
English | 18 Sept. 2015 | ISBN: 1634620968 | 282 Pages | True AZW3 (Kindle)/(EPUB/PDF conv) | 15.44 MB

A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation.
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
Advances in Machine Learning Applications in Software Engineering (repost)

Advances in Machine Learning Applications in Software Engineering by Du Zhang
English | January 30, 2007 | ISBN: 159140942X | 480 Pages | PDF | 12 MB

"Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering also offers readers direction for future work in this emerging research field."