The Machine Learning model is represented by the model parameters. Those parameters are the learnable parameters. Learning happens when these parameters are updated with suitable values and the model is able to solve the given tasks. Training is the process of feeding a training dataset to your model. The training process uses an objective function (example MSE) to get the feedback in each iteration. Since we are trying to improve the accuracy of the model on a given input, and lower the error between model prediction and actual output, we also called training process as a model optimization process.