The problem with this view is that once one gets stuck, which is very quick when one is doing the work for real, one doesn’t have any tools to debug anything except at the most basic level and most probably doesn’t understand anything intuitively enough to even reason about what the underlying problem could be.
I don’t do this work myself, but we’ve hired many interns from bootcamps to do ML, and ones from college with ML projects. The bootcamp grads with no additional background have almost universally hit hard walls once anything gets more complex than using Keras to glue together layers. It’s given me the impression, anecdotally, that bootcamps are largely predatory to take ones money and provide only a veneer of knowledge in the area. This doesn’t seem to apply to people with a CS or math background that took an ML bootcamp to add that dimension to their already-mathematical skillset. But people who have, again only anecdotally in my experience with an n of perhaps only 20, taken a bootcamp to reskill from a totally unrelated and perhaps qualitative field have not had success with a bootcamp alone, but have had success in doing what the above poster recommended in taking university courses in the area.
Very respectfully, if you’re in a boot camp right now, you’re unlikely deep enough into the day to day work of ML to make the assertion you’re making.
I think it depends! If you want to zoom out and take the "systems view" using standard components, then you probably don't need much math. If you want to develop new architectures or algorithms, then you definitely will. The well-trodden paths of ML might have most of their math abstracted away, but in my experience every time you get close to the frontiers, people are using math to understand what's going on or develop new approaches.
It also doesn't really work if you have to tackle a new problem.
I stopped studying maths well before university. I am not some kind of math super genius. But working on my own stuff, which did involve new problems, I was up the creek fairly quickly without a solid mathematical understanding of the techniques I was trying to use.
I don't think the bar is particularly high here. Solid understanding of stats, ESL...but I have seen people shotgunning models (I did this years ago too), and that isn't going to work very long.
Also, I don't really understand why you wouldn't study some of this stuff. Maths as taught in schools treats you like a meat calculator...that isn't fun. But if you are interested in ML, going through Stats, Linear Algebra...it is pretty interesting because there are so many clear connections with your work.
I don’t do this work myself, but we’ve hired many interns from bootcamps to do ML, and ones from college with ML projects. The bootcamp grads with no additional background have almost universally hit hard walls once anything gets more complex than using Keras to glue together layers. It’s given me the impression, anecdotally, that bootcamps are largely predatory to take ones money and provide only a veneer of knowledge in the area. This doesn’t seem to apply to people with a CS or math background that took an ML bootcamp to add that dimension to their already-mathematical skillset. But people who have, again only anecdotally in my experience with an n of perhaps only 20, taken a bootcamp to reskill from a totally unrelated and perhaps qualitative field have not had success with a bootcamp alone, but have had success in doing what the above poster recommended in taking university courses in the area.
Very respectfully, if you’re in a boot camp right now, you’re unlikely deep enough into the day to day work of ML to make the assertion you’re making.