I think now it is the time we get some tutorial/resources/classes on practical implementation of these ML techniques. Enough of Introduction to ML. How to handle large data (say 6000000 rows), how to convert csv/tbv data to different formats needed for different machine learning libraries for e.g. Weka, LibSVM etc.
For an introduction to the broader realm of data input, normalization, modeling, and visualization -- in which ML plays but a part -- you can "preview" Bill Howe's "Introduction to Data Science" class on Coursera[0]; I'm working through the lectures, and I find he gives compelling explanations of what all these parts are, why they're important, and how it all fits together in a larger context.
I took Prof. Howe's course on Coursera and it's a bit of a mixed bag. I can actually see it being better in some respects just going through the content after-the-fact than taking the course as it was run as there were a number of issues with auto-grading of assignments and some of the specific tools choices (like Tableau, which only runs on Windows).
That said, the course covered a lot of ground and touched on a number of different interesting/important topics. Some of the lecture material was a bit disorganized/had errors and didn't flow all that well from one topic to another but there was a lot of good material there, especially if you had enough background to appreciate it. I was comfortable enough but it was obvious that the expectations set by the prereqs were off.
Hopefully the course will run again with most of the kinks worked out and, perhaps, a better level-setting of what's needed to get the most out of the course.
