Guide to Gradient Descent in Machine Learning

Gradient Descent is an iterative optimization algorithm in machine learning that aims to minimize the cost function by finding the global minimum. In this way, it helps machine learning models make more accurate predictions. It keeps updating the model parameters until the cost function is close to, or equal to, zero, in order to achieve the least possible error. The cost function is used to measure the difference between the actual value and the predicted value (also known as the loss). It represents the error and tells us how far the predicted value is from the actual value.