Machine Learning for Data Streams (MIT Press)
Wednesday, 27 June 2018

This book looks at how to work with data streams where information arrives sequentially and at high speed. Authors Albert Bifet, Ricard Gavaldà, Geoff Holmes and Bernhard Pfahringer present algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

<ASIN:0262037793>

Authors: Albert Bifet, Ricard Gavaldà, Geoff Holmes and Bernhard Pfahringer
Publisher: MIT Press
Date: Mar 2018
Pages: 288
ISBN: 978-0262037792
Print: 0262037793
Audience: developers interested in data mining
Level: Advanced
Category: Data Science and Artificial Intelligence 

maclearnds

  • Big data mining
  • Sketching techniques
  • Change
  • Classification
  • Ensemble methods
  • Regression
  • Clustering
  • Frequent pattern mining
  • MOA
  • MOA graphical user interface
  • MOA command line
  • MOA API
  • The development of new methods within MOA.

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

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Publisher: Packt Publishing
Pages: 468
ISBN: 978-1784399689
Print: 178439968X
Kindle: B00YSIL7MA
Audience: Developers new to Machine Learning
Rating: 3.0
Reviewer: Ian Stirk

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Authors: Paul and Harvey Deitel
Publisher: Prentice Hall
Pages: 400

ISBN: 978-0134596327
Print: 0134596323
Kindle: B01KI6RG20
Audience: Intermediate programmers
Rating: 3
Reviewer: Ian Elliot
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