Data Algorithms

This book PDF is perfect for those who love COMPUTERS genre, written by Mahmoud Parsian and published by "O'Reilly Media, Inc." which was released on 13 July 2015 with total hardcover pages 778. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Data Algorithms books below.

Data Algorithms
Author : Mahmoud Parsian
File Size : 52,7 Mb
Publisher : "O'Reilly Media, Inc."
Language : English
Release Date : 13 July 2015
ISBN : 9781491906156
Pages : 778 pages
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Data Algorithms by Mahmoud Parsian Book PDF Summary

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Data Algorithms

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational

Get Book
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