This is code I wrote for courses I taught at Indiana University and then University of British Columbia. The first parts of the code in this tutorial are meant for Python beginners, and the code grows more advanced as in later parts.

In the context of this tutorial, I have added sections covering processing text, use of the Natural Language Toolkit (NLTK), gensim, scikit-learn. I plan to add parts on visualization, numpy, etc.

In addition, I plan to add more advanced code covering practical machine learning issues like vector space models to perform certain tasks like sentiment analysis.

Finally, I also plan to introduce some deep learning tools and provide some relevant code.

The courses teach skills data science skills (i.e, skills at the intersection of natural language processing, applied machine learning, and social media mining).

The code is written primarily in Python 2.7. A migration to Python 3 shoul be straightforward.

Some of the code is written and run during class sessions and so it is shared without much polishing. In some places, you may find some repetition (primarily for pedagogical purposes inside class). I provide some comments, before I push here, as much as I can.