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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

Preface

If you have ever wanted to get into data mining, but didn't know where to start, I've written this book with you in mind.

Many data mining books are highly mathematical, which is great when you are coming from such a background, but I feel they often miss the forest for the trees—that is, they focus so much on how the algorithms work, that we forget about why we are using these algorithms.

In this book, my aim has been to create a book for those who can program and want to learn data mining. By the end of this book, my aim is that you have a good understanding of the basics, some best practices to jump into solving problems with data mining, and some pointers on the next steps you can take.

Each chapter in this book introduces a new topic, algorithm, and dataset. For this reason, it can be a bit of a whirlwind tour, moving quickly from topic to topic. However, for each of the chapters, think about how you can improve upon the results presented in the chapter. Then, take a shot at implementing it!

One of my favorite quotes is from Shakespeare's Henry IV:

But will they come when you do call for them?

Before this quote, a character is claiming to be able to call spirits. In response, Hotspur points out that anyone can call spirits, but what matters is whether they actually come when they are called.

In much the same way, learning data mining is about performing experiments and getting the result. Anyone can come up with an idea to create a new data mining algorithm or improve upon an experiment's results. However, what matters is: can you build it and does it work?

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