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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

More resources

Kaggle competitions:

www.kaggle.com/

Kaggle runs data mining competitions regularly, often with monetary prizes. Testing your skills on Kaggle competitions is a fast and great way to learn to work with real-world data mining problems. The forums are nice and share environments—often, you will see code released for a top-10 entry during the competition!

Coursera:

www.coursera.org

Coursera contains many courses on data mining and data science. Many of the courses are specialized such as big data and image processing. A great general one to start with is Andrew Ng's famous course: https://www.coursera.org/learn/machine-learning/.

It is a bit more advanced than this book and would be a great next step for interested readers.

For neural networks, check out this course: https://www.coursera.org/course/neuralnets.

If you complete all of these, try out the course on probabilistic graphical models at https://www.coursera.org/course/pgm.

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