How about start with a great lecturer like -
Nando de Freitas - https://www.youtube.com/channel/UC0z_jCi0XWqI8awUuQRFnyw
David Mackay -http://videolectures.net/course_information_theory_pattern_r...
or the (sometimes too dense) Andrew Ng - https://www.coursera.org/course/ml
The article addresses this almost immediately.
It's fine to disagree, but crapping a 'Really?' plus some contextless links onto someone who put forth general reasoning for the nature of the recommendations and spent 1300+ words describing expectations, key takeaways and projects for those recommendations is just lame.
I used to hate lectures when I was in school but now I sort of prefer them. It's easier for some reason. It's more passive; you just sit there and listen rather than actively read. It doesn't seem to be slower like others complain, and may even be faster. I read difficult texts very slowly and methodically, and often have to reread stuff.
A great example of this is Andrew Ng's course, even though he is the co-inventor of LDA (complicated Bayes network) he does not explain Bayesian analysis in his course.
"But why textbooks? Because they're one of the few learning mediums where you'll really own the knowledge. You can take a course, a MOOC, join a reading group, whatever you want. But with textbooks, it's an intimate bond. You'll spill brain-juice on every page; you'll inadvertently memorize the chapter titles, the examples, and the exercises; you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn -- the textbook itself becomes a part of your knowledge (the above image shows my nearest textbook). Successful learners don't just read textbooks. Learn to use textbooks in this way, and you can master many subjects -- certainly machine learning."
David Mackay's lectures are incredible and go much further in explaining the material in an understandable fashion than his excellent book "Information Theory, Inference and Learning Algorithms".