I'm currently busy with the neural networks and deep learning specialization on Coursera.
The trick, as far as I can tell, lies in with the various techniques for setting up your data, tuning your hyperparameters, and picking the right architecture for the job. At least, this seems to be the message of the course. It seems to still be a bit of an ad-hoc field. There are a number of techniques and things to try, without there necessarily being more than a shallow theoretical understanding from the experts as to why they actually work.
Then, of course, there are the experts and researchers who come up with entirely new architectures. Now that actually takes skill.
If you do even very simple things rapidly enough, you can get amazing results.
While the author of this article seems to have decent knowledge of the work in this space, his rant really came off (to me) as "Hey look at how smart I am that I can dismiss people who are trying to learn" and made the author appear really insecure.
I believe we should be promoting and fostering a more inclusive environment for those looking to learn ML/AI, in fact if it weren't for some of the (user friendly) tools and friends who taught me a lot, I might have not been so inclined to attempt to write "11 lines of code".