This is something that's almost always glossed over and I'm glad they included it. It's easy to apply ML algorithms to perfect data that doesn't need cleaning and get great results. Finding a productive model when presented with a nuanced, messy problem is a much more difficult task, however, and something most ML crash courses don't focus enough time on.
I think there's a tendency on Hacker News and other tech websites to diminish the importance of having a PhD in ML fields. The problem solving and communication skills you learn during the course of a PhD program are precisely the skills companies value when they're trying to solve hard problems. It's important to know not just how to apply ML algorithms, but when they're appropriate.
I think there's a tendency on Hacker News and other tech websites to diminish the importance of having a PhD in ML fields. The problem solving and communication skills you learn during the course of a PhD program are precisely the skills companies value when they're trying to solve hard problems. It's important to know not just how to apply ML algorithms, but when they're appropriate.