[e-dard]

Hi, machine learning PhD here - one way you can start is by brushing up on some fundamentals. A book such as Machine Learning, by Thom M. Mitchell is a reasonable start.

Also, in terms of applying ML, you could scoot through Andrew Ng's Machine Learning Coursera course (not sure if it's running at the moment, though).

Finally... One tip – typically I have always found that when you want to start apply ML to real-world problemss, start simple and only iterate when the results of your approach are not satisficing. This is usually because all the bleeding edge ML research/techniques don't consider a shit-load of real-world issues, like scaleability, applicability to wide-range of problem, unstructured or noisy data and so on.

[pav3l]

+1 for Tom Mitchell's book. Although it may appear a little outdated, it is an excellent introduction to ML. Highly recommend doing the exercises (in whatever language you'll be working, not necessarily in C), especially the Neural Nets face recognition one. The book doesn't cover SVM, so you might want to learn about those elsewhere. Also if you don't have a good background in basic applied stats (linear models, logistic regression, etc), I suggest you brush up on that as well.

[jordanthoms]

It's funny to see you mention that book, I literally have it next to me right now, studying for my exam next week :)

[ajushi]

Wow thanks for the suggestion. I'll definitely check it out.

[pknerd]

It has 2 much maths