I think most of this is due to the fact that most ML techniques need lots of specific knowledge to apply correctly. Many introductions focus too much on specific algorithms, for example, while ensemble methods are probably best in real-world data. The pactices of building training and test sets, of regularizing, of doing proper feature engineering, and of making structured models (for example) are more easily learned in a mentor-student relationship than by reading blogs (reading papers might take you a long way, but you won't build an intuition as to why things work and why they fail without lots of experimentation or advice from someone who already has this intuition) and/or AI books, and ML books are usually far too technical (or too superficial in the technical side, as is programming collective intelligence).
Most of the ML books I've worked with so far seem a bit overly formal. Steven Marsland's book seems to strike the best balance between theory and implementation I've seen, even if the Python code is a little clumsy.
