Learn the fundamentals of uncertainty quantification in machine learning models through this tutorial that explores methods for assessing and communicating model reliability beyond traditional accuracy metrics. Discover how to quantify prediction uncertainty, understand the difference between aleatoric and epistemic uncertainty, and implement practical techniques for uncertainty estimation in scientific applications. Explore Bayesian approaches, ensemble methods, and Monte Carlo techniques for capturing model uncertainty. Examine real-world case studies where uncertainty quantification proves critical for decision-making in physics and scientific research. Master the interpretation and visualization of uncertainty estimates to better communicate model limitations and confidence levels to stakeholders. Gain hands-on experience with tools and frameworks commonly used for uncertainty quantification in machine learning workflows.