Gradient Descent is an optimization algorithm used to minimize the loss function in machine learning and deep learning models. It is an iterative process that helps find the optimal parameters (weights) for a given model by minimizing the loss function.
Linear regression is one of the simplest and most widely used algorithms for predictive modeling. It models the relationship between a dependent (target) variable and one or more independent (input) variables by fitting a linear equation to the observed data
