# Linear Regression Models: Introduction to Logistic Regression

Machine Learning    |    Intermediate
• 11 Videos | 1h 2m 20s
• Includes Assessment
Likes 27
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• 1.
Course Overview
• 2.
Introducing Logistic Regression
• 3.
The Logistic Regression Curve
• 4.
Logistic Regression and Classification
• 5.
Logistic Regression vs. Linear Regression
• 6.
Logistic Regression in Keras
• 7.
Preparing Data for Logistic Regression
• 8.
Classification using a Logistic Regression Model
• 9.
Preparing Data for a Neural Network
• 10.
Building and Evaluating the Keras Classifier
• 11.
Exercise: An Introduction to Logistic Regression

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