How hard is it to break into ML work without a Master's degree? [D](reddit.com)

I'm currently a software engineer (mostly mobile/iOS development) and have recently started learning machine learning because I genuinely find it interesting, especially the math behind it. I have a fairly strong math background and am comfortable with calculus, probability, and math in general. Right now I'm learning through a combination of Andrew Ng's ML course and Stanford CS229. My plan is to build some projects once I have a stronger foundation. What attracts me to ML is the mathematics behind it. My goal isn't just to use existing libraries to train models and tune hyperparameters; I want to understand the underlying theory, algorithms, and reasoning that make these models work. I'm interested in going deeper into the field rather than treating ML as a black box. That said, I keep seeing ML roles that prefer or require a Master's or PhD, so I'm trying to understand how realistic this path is. For people who have successfully made the switch: Did you have a Master's/PhD, or were you self-taught? How difficult was it to get interviews without an advanced degree? What types of projects helped you stand out? Did you transition into ML engineering first, or directly into more model-focused work? What level of math and statistics do you actually use on the job? If you were starting again today as a software engineer with a strong math background, what path would you follow? I'm looking for honest experiences, including failures and challenges, not just success stories. submitted by /u/Schmosby123 [link] [Kommentare]