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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

Machine learning, statistics, and AI


Machine learning is a term that has various synonyms - names that are the result of either marketing activities by corporates or just terms that have been used interchangeably. Although some may argue that they have different implications, they all ultimately refer to machine learning as a subject that facilitates the prediction of future events using historical information.

The commonly heard terms for machine learning include predictive analysis, predictive analytics, predictive modeling, and many others. As such, unless the entity that publishes material explaining their interpretation of the term and more specifically, how it is different, it is generally safe to assume that they are referring to machine learning. This is often a source of confusion among those new to the subject, largely due to the misuse and overuse of technical verbiage.

Statistics, on the other hand, is a distinct subject area that has been well known for over 200 years. The word...

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