Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Regression

The video tutorial discusses the concept of random variables and their role in regression, particularly when the target variable is continuous. It explains the importance of density functions and conditional expectation in predicting the target variable. Various regression models, such as linear regression, are introduced, highlighting the assumptions made about density functions. The tutorial also touches on the curse of dimensionality, explaining the challenges it poses in estimating probability distributions when dealing with numerous random variables.

What are some techniques mentioned for regression, and how do they differ?

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Describe the relationship between regression models and conditional expectations.

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