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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

Application scenario and goals

There are many use cases for big data, in the public and private sectors.

The most common experience people have using a big-data-based system is in Internet search, such as Google. To run these systems, a search needs to be carried out over billions of websites in a fraction of a second. Doing a basic text-based search would be inadequate to deal with such a problem. Simply storing the text of all those websites is a large problem. In order to deal with queries, new data structures and data mining methods need to be created and implemented specifically for this application.

Big data is also used in many other scientific experiments such as the Large Hadron Collider, part of which is pictured below, that stretches over 17 kilometers and contains 150 million sensors monitoring hundreds of millions of particle collisions per second. The data from this experiment is massive, with 25 petabytes created daily, after a filtering process (if filtering was not used,...

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