• Online, Self-Paced
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

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development