Andreas Mueller

There are various cheat-sheets/decision trees to suggest which machine learning method e.g.

http://www.saedsayad.com/data_mining_map.htm

http://peekaboo-vision.blogspot.co.uk/2013/01/machine-learning-cheat-sheet-for-scikit.html

https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/

However, if you want to really understand how things fit together you're probably best reading one of the standard intro textbooks: Murphy's Machine Learning, Bishop's Pattern recognition and machine learning, Hastie et al's The Elements of Statistical Learning, or Wasserman's All of statistics.

Or Barber's textbook, which is freely available online and has some nice mind-maps/concept-maps/trees at the start of each section: http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook

It's currently down - is it the same one discussed at some length a year ago?

https://news.ycombinator.com/item?id=5831512

Here are some other resources for machine learning, not necessarily restricted to the algorithms implemented in SciKit:

http://eferm.com/wp-content/uploads/2011/05/cheat3.pdf

http://peekaboo-vision.blogspot.ca/2013/01/machine-learning-cheat-sheet-for-scikit.html

http://rise.cse.iitm.ac.in/wiki/index.php/Introduction_to_Machine_Learning

http://mlg.eng.cam.ac.uk/creed/Notes/ML_Compendium.pdf

Other blogs by sklearners:

http://peekaboo-vision.blogspot.fr/

http://www.mblondel.org/journal/category/machine-learning/

http://gael-varoquaux.info/blog/?cat=20

http://fseoane.net/blog/

http://blog.vene.ro/