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Learning Data Mining with Python

You're reading from Learning Data Mining with Python Use Python to manipulate data and build predictive models

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Table of Contents (14) Chapters

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. 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...

More resources

The following would serve as a really good resource for additional information:

Kaggle competitions

URL: 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

URL: 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 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...

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