This tweet is about how I have studied ML and made it my profession. I'll share the resources I've used and the sequence of my study. Straight to point ML pre-requisites(maths) : Linear Algebra, Probability Theory, Calculus, Optimization Theory(optional), Information theory(optional) Linear Algebra: Lecture course by Gilbert Strang Probability theory: MIT 6.041 (it contains parts of Bayesian inference as well) Calculus: your high school and college classes are enough Once basic maths is done then we move to ML. Classical ML : CS229. Either by Andrew NG or someone else. Follow their lecture notes and solve their problem sets. Reference books for classical ML that I followed: PRML by Christopher bishop, Pattern Classification by Duda, Hart and Stork After getting comfortable with classical ML we move to Deep Learning and everything else. Deep Learning and Computer Vision: CS231n. Very good lecture and assignments Reference book: Deep Learning by Ian Goodfellow. This is the best book on deep learning. I’ve read some chapters of it many many times. Beautiful maths and intuitions MLOps: dvc, WandB, MLFlow NLP: I just read hugging face blogs. I haven’t spent much time with classical NLP though. Alignment/AI safety/AI explainability: Anthropic Blogs(I’m a noob in this, just started learning couple months ago) Additionally: Blogs: Lilian Weng(OpenAI)’s blogs, colah’s blogs Additionally: arxiv. I read many papers from arxiv Karas and Tensorflow blogs: for introductory code about modern deep learning frameworks Competitions: Kaggle Cloud compute. GCP/collab/Kaggle notebooks PS: this is not a roadmap. Just what I followed till now and I find it quite structured. Even after 5 years I still find myself learning new stuff everyday.