Course details

Linear Regression Models: An Introduction to Logistic Regression

Linear Regression Models: An Introduction to Logistic Regression


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.0

Lesson 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
  • Course Number:
    it_mllrmddj_04_enus

    Expertise Level
    Intermediate