Logistic regression is a technique used to estimate the probability of an outcome for machine learning solutions. In this 10-video course, learners discover the concepts and explore how logistic regression is used to predict categorical outcomes. Key concepts covered here include the qualities of a logistic regression S-curve and the kind of data it can model; learning how a logistic regression can be used to perform classification tasks; and how to compare logistic regression with linear regression. Next, you will learn how neural networks can be used to perform a logistic regression; how to prepare a data set to build, train, and evaluate a logistic regression model in Scikit Learn; and how to use a logistic regression model to perform a classification task and evaluate the performance of the model. Learners observe how to prepare a data set to build, train, and evaluate a Keras sequential model, and how to build, train, and validate Keras models by defining various components, including activation functions, optimizers and the loss function.

**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