I second learning the statistics/probability basics.
Your first model should always be something that predicts a constant value, or maybe in really complicated cases something like a linear/logistic regression. Then you have a baseline to compare more advanced approaches to. But in order to understand how to use linear regression well, you need to understand how it works in the first place.
Also experiment structure, sampling design, hypothesis testing, etc. will tell you a lot about what conclusions you can and cannot draw from observational data, which is what a lot of ML is about.
Your first model should always be something that predicts a constant value, or maybe in really complicated cases something like a linear/logistic regression. Then you have a baseline to compare more advanced approaches to. But in order to understand how to use linear regression well, you need to understand how it works in the first place.
Also experiment structure, sampling design, hypothesis testing, etc. will tell you a lot about what conclusions you can and cannot draw from observational data, which is what a lot of ML is about.