Learning probability for strong foundation in ML. The books i am following are "First course in probability by Sheldon Ross" and "Probability and Statistics" Michael J. Evans and Je¤rey S. Rosenthal

This is the first time i am studying based on the topics rather than following syllabus. I wanted to understand covariance for calculating similar interest b/w users to suggest the posts viewed by a user in the app i am developing. This took me down the rabbit hole and forced to learn everything required to define covariance. Its talking a lot of time but i feel it's worth it because now i have a strong foundation. Also its nice to follow more than one book because i have no attachment to any of the books. When in college i used to get attached to my notes or the first text book i follow, but studying a topic from many books have no attachment to either and its liberating.