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Learning Data Mining with Python, - Second Edition

You're reading from  Learning Data Mining with Python, - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Pages 358 pages
Edition 2nd Edition
Languages
Concepts

Table of Contents (20) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Data Mining 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Getting Started with Data Mining


In this chapter following are a few avenues that reader can explore:

Scikit-learn tutorials

URL: http://scikit-learn.org/stable/tutorial/index.html

Included in the scikit-learn documentation is a series of tutorials on data mining. The tutorials range from basic introductions to toy datasets, all the way through to comprehensive tutorials on techniques used in recent research. The tutorials here will take quite a while to get through—they are very comprehensive—but are well worth the effort to learn.

There are also a large number of algorithms that have been implemented for compatability with scikit-learn. These algorithms are not always included in scikit-learn itself for a number of reasons, but a list of many of these is maintained at https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets.

Extending the Jupyter Notebook

URL: http://ipython.org/ipython-doc/1/interactive/public_server.html

The Jupyter Notebook is a powerful tool...

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