Model parameters are internal configuration variables estimated or learned directly from training data (e.g., weights in a neural network or coefficients in a regression model) that are required to make predictions.

Model hyperparameters are external configurations specified by the practitioner (e.g., learning rate or the value $K$ in $K$-nearest neighbors) that cannot be learned from data and are used to guide the training process.