I want to teach myself enough machine learning so that I can, to begin with, understand enough to put to use available open source ML frameworks that will allow me to do things like:
Go through the HTML source of pages from a certain site and "understand" which sections form the content, which the advertisements and which form the metadata ( neither the content, nor the ads - for e.g. - TOC, author bio etc )
Go through the HTML source of pages from disparate sites and "classify" whether the site belongs to a predefined category or not ( list of categories will be supplied beforehand ) based on the page content derived from 1
... similar classification tasks on text and pages.
As you can see, my immediate requirements are to do with classification on disparate data sources and large amounts of data.
As far as my limited understanding goes, taking the neural net approach will take a lot of training and maintenance than putting SVMs to use?
I understand that SVMs are well suited to ( binary ) classification tasks like mine, and open source frameworks like libSVM are fairly mature?
Taking forward the question assuming SVMs to be a better answer to classification of the likes of mine, how do I understand things like:
What $R^n$ stands for
or how to transform a space so that it can be broken into two parts etc?2.
In that case, what subjects and topics does a computer science graduate need to learn right now, so that the above requirements can be solved, putting these frameworks to use? Especially those related to SVMs.
I am willing to learn and put in as much effort as I possibly can.
Recommendations from you on learning specific portions of statistics and probability theory is nothing unexpected from my side, so say that if required!
I will modify this question if needed, depending on all your suggestions and feedback.
Cross-posted on SO: