Course Content

  • 2.8.1c Distribution Models

Course Content

FAQs

Data with a Normal Distribution is useful for model construction in Machine Learning. It simplifies math. Models like LDA, Gaussian Naive Bayes, Logistic Regression, Linear Regression, etc., are explicitly calculated from the assumption that the distribution is a bivariate or multivariate normal.

Probability distributions are classified in a variety of ways. The normal distribution, chi square distribution, binomial distribution, and Poisson distribution are a few examples. The various probability distributions serve distinct functions and represent distinct data creation processes.

This bell-shaped curve is known as the Normal Distribution. Because Carl Friedrich Gauss discovered it, we sometimes refer to it as a Gaussian Distribution. We may reduce the Normal Distribution's Probability Density to two parameters: 𝝻 Mean and 𝛔2. This curve is symmetric around the Mean.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere