Course Content

Course Content


Supervised learning, often known as supervised machine learning, is a machine learning and artificial intelligence subcategory. It is distinguished by the use of labeled datasets to train algorithms that accurately classify data or predict outcomes.

  • Prepare Data is the first step in supervised learning.
  • Select an Algorithm.
  • Make a Model.
  • Select a Validation Method.
  • Examine Fit and Update as Needed.
  • Predictions should be made using the Fitted Model.

Unsupervised learning is beneficial for extracting relevant insights from data. Unsupervised learning is very similar to how humans learn to think via their own experiences, making it closer to true AI. Unsupervised learning operates on unlabeled and uncategorized data, making it more significant.

Linear regression is a supervised learning technique that is commonly used in quantitative data prediction, forecasting, and association discovery. It is one of the first learning methods that is still frequently used today.

Supervised Learning techniques are classified into two types: regression and classification. Classification separates data, whereas regression fits data.

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