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Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
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Author (1):
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Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Big data and the context of large-scale Machine learning

I have covered some of the core aspects of big data in my previous Packt book titled Getting Started with Greenplum for Big Data Analytics. In this section, we will quickly recap some of the core aspects of big data and its impact in the field of Machine learning:

  • The definition of large-scale is a scale of terabytes, petabytes, exabytes, or higher. This is typically the volume that cannot be handled by traditional database engines. The following chart lists the orders of magnitude that represents data volumes:

    Multiples of bytes

    SI decimal prefixes

    Binary Usage

    Name(Symbol)

    Value

    Kilobyte (KB)

    103

    210

    Megabyte (MB)

    106

    220

    Gigabyte (GB)

    109

    230

    Terabyte (TB)

    1012

    240

    Petabyte (PB)

    1015

    250

    Exabyte (EB)

    1018

    260

    Zettabyte (ZB)

    1021

    270

    Yottabyte (YB)

    1024

    280

  • Data formats that are referred to in this context are distinct; they are generated and consumed, and need not be structured (for example,...
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