The first step in model training is to divide the dataset into training, validation, and test datasets . The training dataset trains the model, the validation dataset tunes hyperparameters, and the test dataset evaluates the model. The second step is to select an appropriate model on the basis of the nature of the problem. During training, the model recognizes the relationship between the inputs and outputs and minimizes the difference between predicted and actual outputs by iteratively adjusting the model parameters with optimization techniques such as gradient descent.