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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Python Programming and Visualization for Scientists 2nd Ed

Author: Alex DeCaria and Grant Petty
Publisher: Sundog Publishing
Pages: 372
ISBN: 978-0972903356
Print: 0972903356
Audience: Scientists wanting to use Python
Rating: 2
Reviewer: Mike James
Visualization - a difficult topic and difficult to see how to explain the ideas in a book.



How to Grow a Robot: Developing Human-Friendly, Social AI

Author: Mark H. Lee
Publisher: MIT Press
Pages: 384
ISBN: 978-0262043731
Print: 0262043734
Kindle: B0874BMM14
Audience: Developers interested in how robotics and AI can be combined.
Rating: 5
Reviewer: Kay Ewbank

This book sets out to look at how robots can be more human-like, friendly and engaging. [ ... ]


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