I share your sentiment to a fair extent and I have written about it before here on HN.
Yes, for a lot of this stuff you don't need a PhD (and frankly I find that marketing weird for the above course). But you do need strong intuitions, understanding of some CS, and math "savviness" i.e. you don't have to know All The Math now, but you should be able to pick up stuff as needed, when you're trying to understand your problem and/or structuring your solution.
One could learn all of this stuff online today - the amount of good resources out there is crazy. Frankly, I am jealous, because I began working on ML more than 10 yr ago, and we were relatively starved for resources on pretty much all fronts: resources to study from (reading material or videos), affordable compute power, s/w libraries. But unfortunately, despite their abundance today, most people don't take the time to dive deep. Of all the years of me suggesting courses and books to people (when asked), only ONE (or maybe two) person managed to go through them to a fair extent. But there is a significant fraction of the rest, for which reductive messaging like "become a pro in AI in 3 weeks" has been misleading.
As a hiring manager sometimes they are as surprised as me, when an interview doesn't go well, after the resume seemed promising to both sides. And to be very clear, I don't blame them (sure, there are some pretentious opportunists, who flat out lie, but I've found them not to be the norm); all this messaging seems to have created a bubble where often you don't know what you don't know. It's amusing that thrice, rejected candidates reached out me saying that the interview was quite eye opening! I have been at the receiving end too - where 90% of the interview seemed to be about some very specific setting of a library or a method, because that was the conception of ML the interviewer had.
I think people should learn, by whatever means, and create stuff because they can - this is the best kind of learning. Silly projects are great too, if they are fun - if they don't advance your understanding, they might motivate you to be less silly! What's missing in this ecosystem is honest messaging about where your skill levels really are. I don't know how to fix it in a way that also doesn't harm, in some way, the widespread learning/awareness reg ML. On a smaller scale though, I have accepted that this has increased my scope of work in screening resumes: if someone lists her github repo, or an arxiv paper, I actually need to spend time to go through them. I don't see this as noise, but a widening of the spectrum of available ML skills in the market; and I need to put in some effort to place an applicant in this spectrum. I've accepted that this is the flipside of working in a hot area: for the multiple job opportunities accessible to me, I have to put in more thought for hiring. I can't have the luxury of the former without the responsibility of the latter. Although, being lazy, I'd totally want to ;).