The page you are reading is part of a draft (v2.0) of the "No bullshit guide to math and physics."
The text has since gone through many edits and is now available in print and electronic format. The current edition of the book is v4.0, which is a substantial improvement in terms of content and language (I hired a professional editor) from the draft version.
I'm leaving the old wiki content up for the time being, but I highly engourage you to check out the finished book. You can check out an extended preview here (PDF, 106 pages, 5MB).
Machine learning
[ A list of frameworks ]
https://github.com/josephmisiti/awesome-machine-learning
In supervised learning, the machine infers a function from a set of training examples. In unsupervised learning the machine tries to find hidden structure in unlabeled data.
[ Curriculum through online courses ]
http://richardminerich.com/2012/12/my-education-in-machine-learning-via-cousera/
[ List of freely available books ]
http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning
http://efytimes.com/e1/fullnews.asp?edid=121516
https://news.ycombinator.com/item?id=7120391
[ Recommendations ]
http://conductrics.com/data-science-resources/
[ SVD old-school video ]
http://www.youtube.com/watch?v=R9UoFyqJca8
[ Guide to online Data Science courses ]
http://www.bigdatarepublic.com/author.asp?section_id=2809&doc_id=257527&
[ More book recommendations from Quora ]
http://www.quora.com/What-are-some-good-resources-for-learning-about-machine-learning-Why
[ Collection of video lectures ]
http://work.caltech.edu/library/
[ Intro book on data mining ]
http://guidetodatamining.com/
http://www.dmi.usherb.ca/~larocheh/mlpython/tutorial.html#tutorial
http://info.usherbrooke.ca/hlarochelle/cours/ift725_A2013/contenu.html
video lectures
http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html
http://videolectures.net/mlss09uk_cambridge/
Data science curriculum
http://www.mysliderule.com/learning-paths/data-analysis/learn/