A lot of people when starting, they want to go into the fancy and exotic methods - and go straight to learning about things like Deep Neural networks. The thing though, is that at a fundamental level, these more exotic models are composions of more “classical” models, for example, neural nets can be seen as a series of logistic regression problems

Saying that though, first make sure you have a good math back ground - linear algebra (matrix multiplication, understand eigen vectors, some matrix decomposition algorithms), statistics and probability - random variables, joint random variables, density functions for both of them, conditional probability and conditional distributions, and then calculus - understand single variable calculus and multi variable calculus as well, especially the topics of gradients and optimization

Then begin learning some simpler models such as:

linear regression, polynomial regression, decision tree algorithms, etc. then maybe move on to the more exotic models such as RNNs, Transformers, only after you have a strong grasp of the fundamentals.

Especially if you will go the self taught approach, you will not just learn topics once. I have found that as I have relearned topics throughout the years, each time I gain a better understanding of each model(when to use, what kind of data, limitations and advantages, etc.) each time I learn it.

Good luck! DM/comment if you have more questions