The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming.
All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course.
Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! The target audience was originally me, but more broadly, can be someone familiar with programming although no assumption regarding statistics, calculus or linear algebra is made. We go from the very introduction of machine learning to neural networks, recommender systems and even pipeline design. The one thing I will say is that a lot of the later topics build on those of earlier sections, so it's generally advisable to work through in chronological order.
The notes were written in Evernote, and then exported to HTML automatically. As a result I take no credit/blame for the web formatting.
Notes taken from https://www.holehouse.org/mlclass/
Official Notes taken from ml-class.org