Loss functions to evaluate Regression Models

The objective of any machine learning model is to understand and learn patterns from the data which can further be used to make predictions or answer questions or simply just understand the underlying pattern in the data that is otherwise not evident candidly. Most of the time, the learning part is iterative. A model learns some patterns from the data, we test it against some new data that the model did not encounter during training, we see how good or how bad a job it did, we tweak and adjust some parameters, then we put it to test again. This process is repeated until we are presented with a model that is good enough (Although, some real world models can just be satisfactory and make a world of difference). The part where we evaluate and test our model is where the loss functions come into play.