There are various cheat-sheets/decision trees to suggest which machine learning method e.g. http://www.saedsayad.com/data_mining_map.htm http://www.saedsayad.c

There are various cheat-sheets/decision trees to suggest which machine learning method e.g. http://www.saedsayad.com/data_mining_map.htm http://www.saedsayad.com/data_mining_map.htm http://peekaboo-vision.blogspot.co.uk/2013/01/machine-learning-cheat-sheet-for-scikit.html http://peekaboo-vision.blogspot.co.uk/2013/01/machine-learni... https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/ https://azure.microsoft.com/en-us/documentation/articles/mac... 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 http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...