In this talk, I will give an elementary introduction to optimal transport and a metric space of probability measures called the Wasserstein space. After defining gradient flows on metric spaces, we will look at a certain PDE - the continuity equation - from an Eulerian and Lagrangian perspective. I will explain how these PDEs are used in machine learning, employed in generative models for sampling from a probability measure defined on a high-dimensional space.