Assuming you already know some basic linear algebra and calculus, know Python (or R), and have a decent-but-not-advanced grasp of statistics, I'd recommend wor

Assuming you already know some basic linear algebra and calculus, know Python (or R), and have a decent-but-not-advanced grasp of statistics, I'd recommend working through these books. They are very readable and focus on intuitive understanding/practical applications, but give enough technical foundation for you to jump into more specific subfields if needed. Stats & ML - https://www.statlearning.com/ https://www.statlearning.com/ Deep Learning - https://udlbook.github.io/udlbook/ https://udlbook.github.io/udlbook/ Reinforcement Learning - https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf https://web.stanford.edu/class/psych209/Readings/SuttonBarto... As with anything else, people usually fail to learn ML not because of content quality but because of lack of effort/time/consistency. Take handwritten notes, solve exercises, etc., and expect to spend at least a hundred hours on each book.