Course Description
Logistic Regression is a technique used to estimate the probability of an outcome. Discover the concepts and explore how logistic regression is used to predict categorical outcomes.
Learning Objectives
Linear Regression Models: An Introduction to Logistic Regression
- Course Overview
- identify the types of problems which can be solved by logistic regression
- describe the qualities of a logistic regression S-curve and understand the kind of data it can model
- recognize how a logistic regression can be used to perform classification tasks
- compare logistic regression with linear regression
- recall how neural networks can be used to perform a logistic regression
- prepare a dataset to build, train and evaluate a logistic regression model in Scikit Learn
- use a logistic regression model to perform a classification task and evaluate the performance of the model
- prepare a dataset to build, train and evaluate a Keras sequential model
- build, train and validate the Keras model by defining various components including the activation functions, optimizers and the loss function
- employ key classification techniques in logistical regression