| Do you have Linear Algebra knowledge, and Stats 101 knowledge? Then start with ISLR. Then go and watch Andrew Ng Machine Learning course on Coursera (a new version was added in 2022 that uses Python). Then read the sklearn book from its maintainers/core devs. It's from O'Reilly. Then go do the Deep Learning Specialization from deeplearning.ai. Then do fast.ai course. If interested in Deep RL, watch David Silver lectures, then read Deep RL in Action by Zai, Brown. Then do the HF course on Deep RL. This is how you get started. Choose your books based on your personality, needs, and contents covered. And among MOOCs, I highly suggest the one by Canziani, LeCun from NYU. (I loved the 2020 version.) The one taught by Fei Fei Li and Andrej Karpathy is nice. These two MOOCs can substitute classic books based on quality. I have never read cover to cover any of the famous books. I read a lot from them sticking to specific subjects. Get to reading papers, finding implementations. Ng + ISLR will give you good grounds. Fast.ai + deeplearning.ai will give you capability to solve real problems. NYU + Tubingen + Stanford + UMich (Justin Johnson) courses will bring you to the edge. You need a lot of practical experience that aren’t taught anywhere. So, get your hands dirty early. Learn to use frameworks, cloud platforms, etc. Then start reading papers. A crystal clear grasp on Math foundations is a must. Get it if you don't have already. |