Foundations
Core ideas: data, models, training, and evaluation.
Basics
How Machine Learning Works: Data, Models and Learning from Examples
Machine learning fits a model to examples by adjusting parameters to shrink error. Learn how data, loss functions and gradient descent work together.
BasicsWhy Models Overfit and How Training, Validation and Test Sets Catch It
Overfitting means a model memorized training examples instead of general patterns. Data splits, cross-validation and regularization catch and reduce it.