Model Parameters and Hyperparameters in Machine Learning -- What is the difference?

#artificialintelligence 

For example, suppose you want to build a simple linear regression model using an m-dimensional training data set. For more information about this, see the following example: Machine Learning: Python Linear Regression Estimator Using Gradient Descent. Here, kernel specifies the kernel type to be used in the algorithm, for example kernel'linear', for linear classification, or kernel'rbf' for non-linear classification. Here, alpha is the regularization parameter. It is important that during model building, these hyperparameters be fine-tuned in order to obtain the model with the highest quality.