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.

+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 Although it may appear a little outdated, it is an excellent introduction to ML.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.

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

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

I wonder how did they hire you when you had no prior experience? You're lucky. I also want such job.

Exactly what I would like to know too. I have been trying for a while now with not much luck, although I have picked up a decent amount of ML via self study.

How about checking the videos from the machine learning class on coursera by Andrew Ng?

Will do. Thanks!

Not well known but there are some interesting videos on Videolectures http://videolectures.net/site/search/?q=machine+learning

Hi. Congrats on your new job!

As far as advice/suggestions, please consider sharing a few more specifics, so the community may better understand where you're coming from.

Thank you Toshio!

I'll be data mining about the usage of the millions of downloadable applications online.

I would advise you to get started with these books, which are practice-oriented (rather than theory-oriented such as the Mitchell book): http://www.cs.waikato.ac.nz/ml/weka/book.html with the Weka toolkit and/or http://www.liaad.up.pt/~ltorgo/DataMiningWithR/ with the R language and/or or http://shop.oreilly.com/product/9780596529321.do with Python

As for the theory, someone made a nice review of 10 popular ML books here http://zinkov.com/posts/2012-10-04-ml-book-reviews/ and http://www.stat.cmu.edu/~larry/all-of-statistics/index.html is a nice book on inferential statistics.