Parameters in machine learning are special coefficients or weights of the model that are selected and tuned during the training process. They are estimated by fitting the training data to the model and are subsequently used to make predictions on new data.In contrast, hyperparameters are set before the training process. They are unique settings that govern the learning process in ML and directly impact the model parameters that an algorithm learns.Hyperparameters can’t alter themselves while the model is in its learning or training stage, and they don’t make it into the final model. Moreover, it’s impossible to discern the values of the hyperparameters used during training just by examining the model; only the learned model parameters are evident. Examples of hyperparameters include the learning rate, the number of hidden layers in a neural network, and the regularization parameter.