A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data. It is required by the model when making predictions and estimated or learned from data. It is often not set manually by the practitioner. E.g. The coefficients in a linear regression or logistic regression; the weights in an artificial neural network, etc.

A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data. It is often used in processes to help estimate model parameters. It is often specified by the practitioner. We may use rules of thumb, copy values used on other problems, or search for the best value by trial and error. E.g. The learning rate for training a neural network; the K in K-nearest neighbors, etc.