Have you taken undergraduate-level linear algebra, multivariable calculus and probability? Those are the prerequisites if you want to approach things a bit more rigorously. If you've covered them, get something specific to ML.
I like Hands-On ML... by Geron as a decent intro to ML book. FastAI seems a bit overrated to me - I didn't like that it uses its own helper library or the teaching style but it obviously works for other people.
Then there's more exhaustive books on theory - Elements of Statistical Learning, Pattern Recognition and Machine Learning, Bayesian Reasoning and Machine Learning, Murphy's books on probabilistic ML etc. But obviously the theory books have a lot of overlap with each other so there will be lots of material to skip after you've read one or two of them.
I like Hands-On ML... by Geron as a decent intro to ML book. FastAI seems a bit overrated to me - I didn't like that it uses its own helper library or the teaching style but it obviously works for other people.
Then there's more exhaustive books on theory - Elements of Statistical Learning, Pattern Recognition and Machine Learning, Bayesian Reasoning and Machine Learning, Murphy's books on probabilistic ML etc. But obviously the theory books have a lot of overlap with each other so there will be lots of material to skip after you've read one or two of them.