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Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

Book Specification

Binding Hardcover
Language English
Number Of Pages 288
Author Albert Bifet
Publisher MIT Press
Isbn-10 0262037793
Isbn-13 9780262037792
Dimension 17.78*1.75*22.86

Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

Albert Bifet's Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source Software framework. Today many information sources-including sensor networks, Financial markets, social networks, and Healthcare monitoring-are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents 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. The book first offers a brief introduction to the topic, covering big data mining, Basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential Reference for Readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new Algorithms for MOA.
Store Price Buy Now
Amazon, Hardcover Rs. 3100.67

Why you should read Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series) by Albert Bifet

This book has been written by Albert Bifet, who has written books like Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series). The books are written in Computer Science category. This book is read by people who are interested in reading books in category : Computer Science. So, if you want to explore books similar to This book, you must read and buy this book.

How long would it take for you to read Machine Learning for Data Streams – with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

Depending on your reading style, this is how much time you would take to complete reading this book.

Reading Style Time To Finish The Book
Slow 57 hours
Average 28 hours
Good 19 hours
Excellent 9 hours
So if you are a Reader belonging in the Good category, and you read it daily for 1 hour, it will take you 19 days.
Note: A slow reader usually reads 100 words per minute, an average reader 200 words per minute, an average reader 300 words per minute and an excellent leader reads about 600-1000 words per minute, however the comprehension may vary.

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