Data Science and Machine Learning (Theory and Projects) A to Z - Optional Estimation: Parametric Distributions

The video tutorial introduces parametric and non-parametric distributions, explaining how parametric distributions are defined by parameters like mean and standard deviation for normal distributions. It contrasts these with non-parametric distributions, which do not follow a specific parametric function. The tutorial also covers kernel density estimation (KDE) as a method for modeling non-parametric distributions, using Python's seaborn library. The video concludes with a preview of upcoming content on parameter estimation methods.

What are the parameters associated with the binomial distribution?

In what scenarios might real data be modeled using parametric distributions?

What methods can be employed to estimate parameters for parametric distributions?