introduction to scikit-learn lesson

This is an assignment for instructors training 8.5.

The idea is to give a minimal introduction to `scikit-learn`, possibly in 10 minutes, focusing on a single example of regression.
I chose regression because I think it is more familiar than classification to most scientists.

Here is the rendered notebook (always updated to the last commit on this PR):
http://nbviewer.ipython.org/github/zonca/bc/blob/intro-sklearn-abalone/intermediate/python/xx-sklearn-abalone.ipynb

I would like to ask first some general feedback, then later we can focus on the details:

* I would like this to be suitable for novices, however I am using `pandas`...would you have any recommendation?
* Do you think the Abalone age regression is a suitable example?
* For explaining Decision Trees I think the best is using an example, so I am using a tiny Decision Tree from the Iris dataset, do you think it is appropriate?
* I preferred not to explain supervised/unsupervised learning, and just plainly focus on supervised learning, I think that is not essential for a basic introduction. Any thought?