We'll start with data manipulation using Pandas for loading and cleaning data. We'll then use Scikit-learn to make predictions. The materials can be found in the IPython Notebook below.
https://github.com/savarin/python_for_ml
To get the most out of the tutorial, do follow along with the IPython Notebook. The required Python libraries can be installed with the following commands in Terminal:
pip install --upgrade pip
pip install scipy
pip install numpy pandas sklearn ipython
pip install jupyter
Clone the material in this tutorial using git as follows:
git clone git://github.com/savarin/python_for_ml.git
You should be able to type the following in your terminal window and see the notebook panel load in your web browser.
jupyter notebook
For those starting Python from scratch, completing the first lessons of Units 1-5, 7-8 and 10 on Codecademy is highly recommended (~3-4 hours).
https://www.codecademy.com/learn/python
Post-tutorial, the following links are recommended next steps.
http://pandas.pydata.org/pandas-docs/stable/10min.html
https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/info (Chapter 8)
https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-8
The list of methods can be found here.