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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Chapter 6.  Machine Learning

We are the consumers of machine learning every day, whether we notice or not. E-mail providers such as Google automatically push some incoming mails into the Spam folder and online shopping sites such as Amazon or social networking sites such as Facebook jump in with unsolicited recommendations that are surprisingly useful. So, what enables these software products to reconnect long lost friends? These are just a few examples of machine learning in action.

Formally, machine learning is a part of Artificial Intelligence (AI) which deals with a class of algorithms that can learn from data and make predictions. The techniques and underlying concepts are drawn from the field of statistics. Machine learning exists at the intersection of computer science and statistics and is considered one of the most important components of data science. It has been around for quite some time now, but its complexity has only increased with increase in data and scalability...

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