I agree you often don't need to know some of the guts of the algorithms, but what they're doing and how to interpret their results is important imo. There is a lot of misuse of statistics, or at least sloppy use of statistics, in the "applied data mining" field. In particular, when interpreting the results you usually need to be careful with paying attention to what assumptions went into generating them, or else it's easy to make stronger claims than the data really warrants.
And here I totally agree. I guess the message that needs to be made clear is that doing machine learning is really quite easy and really nothing special, doing something meaningful with the numbers that come out from your program is the hard part that should be focused on.
