Regression Math: Using Gradient Descent & Logistic Regression

Gradient descent is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning. Use this course as an introduction to gradient descent examining how it can be used in a wide variety of optimization problems. Explore how it can be used to perform linear regression carefully studying the matrix equations used to compute the gradients and updating the model parameters using the gradients as well as the learning rate hyperparameter. Finally apply a form of gradient descent known as stochastic gradient descent to fit an S-curve thus implementing logistic regression on a data set. By the end of the course youll be able to assuredly implement logistic regression using gradient descent.