Focus on data transformation and scenarios, not math, if you want to get into real-world Machine Learning. Yes, you’ll eventually have to learn some math, but the abstraction level within ML engineering has already moved on.
Learning the theory and math at the foundations of Machine Learning is like learning the architecture and instructions of CPUs. Yes, it helps A LOT. Eventually. But it’s not the wisest first step.
You’ll become much more fluent, much faster, by doing Machine Learning. And doing ML in the real world is more like remodeling a room than it’s like building a building from scratch. Or more like trying on outfits rather than sewing them.
Real world ML is generally about evolving your own data into being compatible with an existing ML solution and then further evolving things. And that’s a faster and more fun way to learn.