This is the fundamental part of modeling as we introduce the data to different ML models and train the model so that it can learn the representations of the data holistically. We can see how a model is making progress during its training using training error. We often bring validation error (which means we validate the model training simultaneously) into this picture as well, which is a standard practice. Most of the modern libraries today allow us to do this and we will see it in the upcoming chapters of this book. We will now discuss some of the most commonly used error metrics.