I'm relatively new to Python so I don't have anything that I've closely studied...mostly things I've bookmarked that have been submitted to HN. The attractiveness of Python to me is the huge scientific programming community behind it, plus its human-friendly syntax...if R was more readable by me, I'd prefer to work with just one language.

So in terms of Python resources:

- The classic NLTK book: http://www.nltk.org/

- A Programmer's Guide to Data Mining: http://guidetodatamining.com/

- Hitchhiker's Guide to Python: http://docs.python-guide.org/en/latest/index.html

- Statistical Inference for Everyone: http://web.bryant.edu/~bblais/statistical-inference-for-ever...

- Frequentism and Bayesianism: A Python-driven Primer: http://arxiv.org/abs/1411.5018

- Probabilistic Programming and Bayesian Methods for Hackers: http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabil...

- Software Carpentry's primer: http://software-carpentry.org/v5/novice/python/index.html

- And of course, LPTHW: http://learnpythonthehardway.org/

I also came across Practical Python and OpenCV book and have found it really useful for implementing computer-vision exercises...it's not free, but the author has a blog where he regularly posts insightful examples: http://www.pyimagesearch.com/

(I haven't created any class-specific lessons but will definitely post them when I have them ready)