During the course of this book, there were lots of avenues not taken, options not presented, and subjects not fully explored. In this Appendix, I've created a collection of next steps for those wishing to undertake extra learning and progress their data mining with Python. Consider this Hero mode, the second question, of the book.
This appendix is broken up by chapter, with articles, books, and other resources for learning more about data mining. Also included are some challenges to extend the work performed in the chapter. Some of these will be small improvements; some will be quite a bit more work—I've made a note on those tasks that are noticeably more extensive than the others.
http://scikit-learn.org/stable/tutorial/index.html
Included in the scikit-learn documentation is a series of tutorials on data mining. The tutorials range from basic introductions to toy datasets, all the way through to comprehensive tutorials on techniques used in recent research.
The tutorials here will take quite a while to get through—they are very comprehensive—but are well worth the effort to learn.
http://ipython.org/ipython-doc/1/interactive/public_server.html
The IPython Notebook is a powerful tool. It can be extended in many ways, and one of those is to create a server to run your Notebooks, separately from your main computer. This is very useful if you use a low-power main computer, such as a small laptop, but have more powerful computers at your disposal. In addition, you can set up nodes to perform parallelized computations.More datasets are available at:
http://archive.ics.uci.edu/ml/
There are many datasets available on the Internet, from a number of different sources. These include academic, commercial, and government datasets. A collection of well-labelled datasets is available at the UCI ML library, which is one of the best options to find datasets for testing your algorithms.
Try out the OneR algorithm with some of these different datasets.