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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (16) Chapters Close

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Introducing MLlib


Fortunately, you don't have to do things the hard way in Spark when you're doing machine learning. It has a built-in component called MLlib that lives on top of Spark Core, and this makes it very easy to perform complex machine learning algorithms using massive Datasets, and distributing that processing across an entire cluster of computers. So, very exciting stuff. Let's learn more about what it can do.

Some MLlib Capabilities

So, what are some of the things MLlib can do? Well, one is feature extraction.

One thing you can do at scale is term frequency and inverse document frequency stuff, and that's useful for creating, for example, search indexes. We will actually go through an example of that later in the chapter. The key, again, is that it can do this across a cluster using massive Datasets, so you could make your own search engine for the web with this, potentially. It also offers basic statistics functions, chi-squared tests, Pearson or Spearman correlation, and some...

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