What is the difference between parametric and non-parametric learning in depth?

Parametric models assume a fixed number of parameters, while non-parametric models grow in complexity with data.

Parametric models (like linear regression, logistic regression) assume a fixed functional form and learn a finite set of parameters. Non-parametric models (like KNN, decision trees) do not assume a fixed structure and adapt their complexity based on data size. Parametric models are faster and more interpretable, while non-parametric models are more flexible but computationally expensive.