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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

Who this book is for

This book is for programmers who want to get started in data mining in an application-focused manner.

If you haven't programmed before, I strongly recommend that you learn at least the basics before you get started. This book doesn't introduce programming, nor does it give too much time to explain the actual implementation (in code) of how to type out the instructions. That said, once you go through the basics, you should be able to come back to this book fairly quickly—there is no need to be an expert programmer first!

I highly recommend that you have some Python programming experience. If you don't, feel free to jump in, but you might want to take a look at some Python code first, possibly focusing on tutorials using the IPython Notebook. Writing programs in the IPython Notebook works a little differently than other methods such as writing a Java program in a fully fledged IDE.

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