Maybe it's a difference in interpretation, but to me, an 'intensive course' for people who are working is a few weeks of classroom-led instruction. That's the interpretation I was dismissive of.
Without wanting to make this into a discussion about what 'a productive machine learning guy' needs to know, I would say that you need to be able to understand, implement and calibrate most state of the art machine learning techniques. So then you need 3-4 semesters of undergrad maths (algebra 1 & 2, calculus 1 & 2, statistics, linear algebra), a theoretical machine learning specific course (course as in 'university course', so 8-12 weeks with 1/2 lectures per week plus lab sessions, plus homework/exercises, plus exam; this could maybe be consolidated into half that time for a more experienced person who commits himself 8 hours a day plus half a day on the weekends), an operations research or similar course, and maybe a numerical computing course or short course.
All of this is assuming that the candidate already has a solid grasp of computer programming, including some data structures, algorithms, distributed systems maybe. Plus good practical skills, but that should be the least of the problems.
This is at least one year of full time study, for a gifted person who can really commit to the program. Realistically it's 1 1/2 or 2 years for most people.
Somebody without a college degree will likely not know any of these topics (I mean the things from my second paragraph), save maybe some high school calculus and stats. Maybe they've used R a bit and have done some numerical computing or so. But that's a far way from becoming what I would call 'a productive machine learning guy'.
edit: typo