The first part of the workshop will introduce the different types of uncertainty, like aleatoric and epistemic ones, and define the typical uncertainty metrics. The second part will detail several techniques to define and estimate uncertainty in machine learning projects: Bootstrapping, Ensemble methods, Bayesian approaches and Quantile regression. The third part of the workshop is dedicated to a more recent and promissing technique: Conformal Prediction. The last part is a hands-on session in python to experiment with these diffent techniques, with simple datasets.