Really? I found descriptions of Linear and Logistical Regression, as well as the descriptions of Support Vector Machines to both explain very well why they work. The Neural Networks description could have been a bit more thorough, but I still understand them much better now than I did before.
This is a great tip. Actually, I was kind of hoping that Ng's course would be focused more on implementation and less on the why, not that the theory is not important, it's just that I have found a lot of other great places on the web to get the theory for free (like the lectures you mentioned).
I enjoyed far more Tom Mitchell's lectures, they gave me a much better understanding of the algorithms described:
http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
There are some comments on HN about it
http://news.ycombinator.com/item?id=3199718