Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Methods: Regression

This video introduces regression as a type of supervised learning where the target is a continuous value rather than categorical. It explains the concept of features and targets, using house price prediction as an example. The video also discusses the possibility of having multiple targets in regression problems, such as predicting maximum and minimum temperatures. The session concludes with a preview of practical coding in a Jupyter notebook, to be covered in the next video.

What is meant by supervised learning in the context of regression?

Explain the difference between a target and a label in supervised learning.

What are some examples of features that might be used to predict the price of a house?

What are the potential targets in a regression problem, and how can they vary?

Describe what is meant by regression data and provide an example.