# Learning For mastery, a formal education is also required; either by way of open-courseware, or by paying an institution. I have done both, and overall benefitted as a result. - [X] Zero to Hero - Andrej Karpathy - [X] Neural Networks and Deep Learning - Michael Nielsen - [X] UNSW AI - [X] UNSW Machine Learning and Data Mining - [X] UNSW Deep Learning and Neural Networks - [X] UNSW Computer Vision - [ ] Stanford CS229 (Machine Learning) - [ ] Stanford CS230 (Deep Learning) - [.] Mathematics for Machine Learning, Ong et al. - [o] HOML (Hands on Machine Learning) - [ ] All of Statistics, Larry Wasserman - [X] Coursera Machine Learning Specialisation - [X] Coursera Deep Learning Specialisation # Lessons As I progress through more Machine Learning, I realise important facts: 1. always split into train, validation and testing. you will need to tune the hyperparameters. 2. always standardise features before CV (cross validation). check the math, alphas affect coefficients. 3. `accuracy_score` is only for _classification_ problems, not for _regression_ problems!