My last company dabbled with the usual web data mining, but with some text mining / NLP twist for ads. Then later tried to apply machine learning but it closed down on our locality.
I wish to continue learning and trying to research on it. I am horrible at math, but I'm trying to change that, so I'm trying to re-learn high school algebra as a starting point. I know some machine learning resources on the web speak heavily using linear algebra, so that would be my long term goal.
What I wish to be is an active researcher on the field of machine learning. An independent researcher, which I know is possible since, from what I understood, one doesn't need government approval or very huge funds to start with it.
Going to university is out of the question. I have to do researching myself with my own resources.
Any help would be highly appreciated ;-)
To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. http://www.stanford.edu/class/cs229/materials.html http://www.youtube.com/watch?v=UzxYlbK2c7E
If you just want to use the libraries, you can stop here.
If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. http://www.athenasc.com/nonlinbook.html
Then, you can try to finish EE364 and watch the videos. http://www.stanford.edu/class/ee364a/ http://www.youtube.com/watch?v=McLq1hEq3UY
If you want to roll your own algorithms, you have to know some optimization tools. http://cvxr.com/cvx/
And there is some statistics knowledge you have to fill in. I used these: http://www.stat.umn.edu/geyer/5101/ http://www.stat.umn.edu/geyer/5102/ R is used in the courses.