Recently, I’ve had my new book, Learning Data Mining with Python published. The book is an introduction to data mining for people that have the basics of programming already. Little time is given to the details of how code works, allowing more time for actually learning the algorithms and how they work.

In the book, I cover prediction, classification, affinity analysis, clustering, and lots of other algorithm types. However, each algorithm is matched with a real-world based example on how to use it, and some examples on where else the concepts could be applied. Each chapter has its own contained code sample, meaning that by the end of the book, you’ll have twelve different data mining projects up and running.

The book is available on Amazon and directly from the publisher Packt. If you want to have a peek inside first, you can read some of it at Google Books.

The chapter titles are:

  • Chapter 1: Getting Started with Data Mining
  • Chapter 2: Classifying with scikit-learn Estimators
  • Chapter 3: Predicting Sports Winners with Decision Trees
  • Chapter 4: Recommending Movies Using Affinity Analysis
  • Chapter 5: Extracting Features with Transformers
  • Chapter 6: Social Media Insight Using Naive Bayes
  • Chapter 7: Discovering Accounts to Following Using Graph Mining
  • Chapter 8: Beating CAPTCHAs with Neural Networks
  • Chapter 9: Authorship Attribution
  • Chapter 10: Clustering News Articles
  • Chapter 11: Classifying Objects in Images Using Deep Learning
  • Chapter 12: Working with Big Data
  • Appendix A: Next Steps…
Image attribution