# Linear Regression Models: Introduction to Logistic Regression

Machine Learning    |    Intermediate
• 11 videos | 57m 50s
• Includes Assessment
Rating 4.3 of 13 users (13)
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

• In this video, find out how to identify the types of problems which can be solved by logistic regression.
• 3.  The Logistic Regression Curve
Upon completion of this video, you will be able to describe the qualities of a logistic regression S-curve and understand the data it can model.
• 4.  Logistic Regression and Classification
Upon completion of this video, you will be able to recognize how to use a logistic regression to perform classification tasks.
• 5.  Logistic Regression vs. Linear Regression
To compare logistic regression with linear regression, find out how to do so.
• 6.  Logistic Regression in Keras
Upon completion of this video, you will be able to recall how neural networks can be used to perform a logistic regression.
• 7.  Preparing Data for Logistic Regression
In this video, you will learn how to prepare a dataset, build, train, and evaluate a logistic regression model in Scikit Learn.
• 8.  Classification using a Logistic Regression Model
In this video, you will use a logistic regression model to perform a classification task and evaluate the performance of the model.
• 9.  Preparing Data for a Neural Network
Learn how to prepare a dataset to build, train, and evaluate a Keras sequential model.
• 10.  Building and Evaluating the Keras Classifier
During this video, you will learn how to build, train, and validate the Keras model by defining various components including the activation functions, optimizers, and the loss function.
• 11.  Exercise: An Introduction to Logistic Regression
In this video, you will use key classification techniques in logistical regression.

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