What is the difference between a model parameter and a learning algorithm$$s hyperparameters? Explain the concept with respect to Logistic Regression $[2$ marks$]$

Model parameters $-$ weights associated with the features in the input data. eg$.$ w$,$ b in LR

Hyperparameters control the learning process, need to be set manually, eg$,$ learning rate $($eta$),$ Regularization parameter $($lambda$)$ in LR