Hello sklearners,
I apologize in advance if this is regarded as a shameless plug, but...
I rewrote the R exercises for the statlearning course (from Stanford
University, conducted by professors Trevor Hastie and Rob Tibshirani) into
a set of 9 python notebooks. All the algorithms used in my code are from
sklearn. I thought that this may be a good resource for people starting
with sklearn, so letting folks here know...
I have written a blog post about it:
http://sujitpal.blogspot.com/2014/05/ipython-notebooks-for-statlearning.html
If you want to skip the editorializing, the code is in github here:
https://github.com/sujitpal/mlia-examples/tree/master/src/statlearning_notebooks
and the data is here:
https://github.com/sujitpal/mlia-examples/tree/master/data/statlearning_notebooks
The README.md page on the code directory has links to render the notebooks
onnbviewer.ipython.org.
Would appreciate any suggestions for improvement as well.
Thanks
Sujit
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