Like any topic/skill, it can be learnt, but only if you spend significant time and effort by doing projects, exercises, asking questions (stackexchange, etc.). It's very important to pay attention to fundamentals and thinking from scratch rather than mastering a laundry list of tips/tricks, because fundamental ideas can be composed in different ways and adapted to a new situation. The fundamentals here would be probability, statistics, linear algebra, optimisation.

I started my career in machine learning with absolutely no knowledge in it. It is definitely some thing that you can learn on the job. You do need a background in linear algebra/ statistics to understand the theory behind different algorithms that will help you decide what algorithm to choose (SVM vs Random Forest for ex.).

Like suggested in the other comment, the best place to start is probably by working on projects with open data sets. Try experimenting with different algorithms, feature engineering techniques. This is especially important because there are plenty of algorithms and identifying which algorithm works for which kind of data set is useful.

I started with no ML experience per se, but I already had a background in linear algebra, differential equation modeling and stochastic processes.