Python framework for Coursera ML homeworks
The Coursera ML class uses Octave for their homework assignments.Arguably Octave is great for novice students getting started quickly with a single package that allows them to quickly manipulate matrices etc. However many students in the PGM and ML courses have wondered if there couldn't be a Python alternative based on SciPy or other similar frameworks for Python.
While it may not be possible to create a perfect replacement for Octave many more programming experienced students would love to be working with Python rather than Octave, particularly because of support for testing, continuous integration and so forth.
This repository is a placeholder for any and all attempts to replicate parts of the Coursera PGM and ML homework assignment code in Python. It was created as part of this discussion in the PGM class, which annoyingly you will be unable to access if you didn't sign up for PGM first time around, so please email py-coursera@googlegroups.com for a summary if you're interested. By the time you read this there may a good summary in the archives:
https://groups.google.com/forum/#!forum/py-coursera
In the first instance we'll attempt to recreate the ML/PGM Octave tutorials using the Enthought SciPy/Python distribution. Here's the start of a transcript based on Prof Ng's Octave transcript
Let me elaborate, IPython is Python with a high powered interactive shell:
which we've pulled in as part of the one click install Enthought distribution.,
This ipython_session thing now mostly complete due to some great work by Jeff Tratner.
The rest (PGM and ML assignments + infrastructure) is TODO - We'll keep working on it, but all help appreciated it. Please let us know your github id if you'd like to collaborate. Feel free to email sjoseph@hpu.edu.
Many thanks to all Contributors including Jeff Tratner, Arthur Dent, Andrew Clegg