Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

by Mahmoud Parsian

Learn the algorithms and tools you need to build MapReduce applications with Hadoop for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. With this practical book, Author Mahmoud Parsian, head of the big data team at Illumina, takes you step-by-step through the design of machine-learning algorithms, such as Naive Bayes and Markov Chain, and shows you how apply them to clinical and biological datasets, using MapReduce design patterns.Apply MapReduce algorithms to clinical and biological data, such as DNA-Seq and RNA-SeqUse the most relevant regression/analytical algorithms used for different biological data types

Genres: Computers, Computer Science

778 pages, Paperback

First published August 1, 2014

Reviews

[Xianshun Chen · Rating 4 out of 5]

Very nice book which teaches how to implement mechine learning and data mining techniques such as NBC, recommender, clustering, etc. Implemented in java, the book provides codes in both hadoop mapreduce and apache spark in simple-to-understand and clean manner. Have re-coded most of the algorithms in the book except for chapters dealing with some of the bio stuff which i am not particularly interested at the moment.

[Delhi Irc]

DL027800 ND6