In this video we look at probabilities and logistic regression. We start with a quick refresher on basic probabilities and we cover Binary Logistic Regression. We talk about the rules of probability, derive the Logistic Regression model, and look at techniques for estimating the model parameters. We discuss certain principal assumptions of the Logistic Regression model, and we see the model in action and cover how it is typically used with a classification dataset.
Link to the slides and Python code:
Link to the Machine Learning Study Group meetup page: