Explains the Girsanov’s Theorem for Brownian Motion using simple visuals. Starts with explaining the probability space of brownian motion paths, and once the probability measure is introduced, then shows how the change of probability measure looks like visually. The video ends with outlining the relationship between conditional expectation under the two measures.