I want to learn math for machine learning, and I want to start with informal set theory.
I was reading 'naive set theory' (1960) by halmos, and it didn't seem to contain modern set notations.
If anyone knows a good material for learning informal set theory, please leave a comment.
That being said, I do not mind some rigor as long as it helps me with statistics, calculus, and other math fields used in machine learning.
You can see the book "Book of Proof" of Richard Hammack; it have many diagrams and pics. The chapter about cardinals is very educational.
P.S.: Machine leaning is more about Linear Algebra and Probability Theory.