# 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!