Material for open source machine learning practical
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01 - Why scikit-learn.ipynb
02 - First Steps.ipynb
03 - Unsupervised Transformers.ipynb
04 - API Summary.ipynb
05 - Cross-validation.ipynb
06 - Grid Searches for Hyper Parameters.ipynb
07 - Preprocessing and Pipelines.ipynb
08 - Working With Text Data.ipynb
08.5 Feature Union.ipynb
09 - Out Of Core Learning.ipynb
10 - Other tools.ipynb
11 - Pystruct.ipynb
README.md
intro_to_structured_prediction.pdf

README.md

Machine Learning Open Source Software

Notebooks / material for the machine learning software practical.

This tutorial covers some of the tools provided by scikit-learn, as well as an introduction to pystruct.

You need at least scikit-learn v0.15 and ipython v3.0 for this practical. If you don't already have an installation, I recommend using the anaconda distribution: http://continuum.io/downloads

For the last part, you will also need pystruct, which you can install using

pip install pystruct

If it errors on installing PyQPBO or AD3, don't worry too much.

Check out the documentation of scikit-learn at http://scikit-learn.org/dev/documentation.html and pystruct at http://pystruct.github.io