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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
Languages
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

What is machine learning?


Machine learning is not a new subject; it has existed in academia for well over 70 years as a formal discipline, but known by different names: statistics, and more generally mathematics, then artificial intelligence (AI), and today as machine learning. While the other related subject areas of statistics and AI are just as prevalent, machine learning has carved out a separate niche and become an independent discipline in and of itself.

In simple terms, machine learning involves predicting future events based on historical data. We see it manifested in our day-to-day lives and indeed we employ, knowingly or otherwise, principles of machine learning on a daily basis.

When we casually comment on whether a movie will succeed at the box office using our understanding of the popularity of the individuals in the lead roles, we are applying machine learning, albeit subconsciously. Our understanding of the characters in the lead roles has been shaped over years of watching...

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