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Practical Big Data Analytics

You're reading from   Practical Big Data Analytics Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781783554393
Length 412 pages
Edition 1st Edition
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Concepts
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Author (1):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
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Table of Contents (13) Chapters Close

Preface 1. Too Big or Not Too Big FREE CHAPTER 2. Big Data Mining for the Masses 3. The Analytics Toolkit 4. Big Data With Hadoop 5. Big Data Mining with NoSQL 6. Spark for Big Data Analytics 7. An Introduction to Machine Learning Concepts 8. Machine Learning Deep Dive 9. Enterprise Data Science 10. Closing Thoughts on Big Data 11. External Data Science Resources 12. Other Books You May Enjoy

Categories of machine learning


Arthur Samuel coined the term machine learning in 1959 while at IBM. A popular definition of machine learning is due to Arthur, who, it is believed, called machine learning a field of computer science that gives computers the ability to learn without being explicitly programmed.

Tom Mitchell, in 1998, added a more specific definition to machine learning and called it a, study of algorithms that improve their performance P at some task T with experience E.

A simple explanation would help to illustrate this concept. By now, most of us are familiar with the concept of spam in emails. Most email accounts also contain a separate folder known as Junk, Spam, or a related term. A cursory check of the folders will usually indicate the presence of several emails, many of which were presumably unsolicited and contain meaningless information.

The mere task of categorizing emails as spam and moving them to a folder involves the application of machine learning. Andrew Ng highlighted...

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