Elements of Statistical learning is from the same authors (mostly) as ISLR but it's longer and goes a bit deeper on the theory. Also, Computer Age Statistical Inference which is newer and covers more material.
Other big titles - Pattern Recognition and Machine Learning (Bishop), Machine Learning: A Probabilistic Perspective (Murphy) - these last two weren't for me but different people respond to different approaches so check them out especially if the first two books don't click for you.
Could probably go through it again but I’d also like more of a theoretical background.
Isn’t OLS a specific case of MLE, for example ?
Anyway, I’d like to understand machine learning and statistical inference more deeply.
I’d be happy to get the answers by reading a handful of books.