I am often asked what to look at if somebody wants to get started with Machine Learning. Usually I sent people to Coursera "Machine Learning" class by Andrew Ng. It's like a litmus test - after taking that one, people usually get a feeling if Machine Learning is something they want to continue with or not.
However, if you decide to continue, what's next? What kind of knowledge\skills to look at? What are buzzword in all those learning materials?
And than I found this blog post, which IMHO, summarize it all pretty well! Even addressing nesessity to understand Linear Algebra (while I usually take it for granted and never mention, but in fact - one have to know Linear Algebra. It's a corner stone of any engeneering skill) So here it is, enjoy:
Do not despair if it seems too much to deal at once. The learning approach can be bottom-up i.e. from theory to practice... but top-down works as well! I.e. get yourself a case and work down the limited scope of the theory nesessary to understand and develop the solution.