Bias/variance, metrics, scaling, and generalization fundamentals.
Open Classical ML Foundations →Train/validation/test, normalization, and deep learning specific notes.
Open Deep Learning Foundations →Supervised vs unsupervised, data loading, EDA, and framework comparisons.
Open General Concepts →