These pedagogical notes provide an introduction to nonlinear filtering for diffusion processes. The objective of nonlinear filtering is to estimate an unobserved stochastic state from partial and noisy observations, and thereby characterize the conditional distribution of the state given the observation history. After introducing the state-observation model, we develop the main tools of the theory, including Markov semigroups, infinitesimal generators, and change-of-measure methods. This leads naturally to the Kallianpur-Striebel formula, the Zakai equation for the unnormalized filter, and the Kushner-Stratonovich equation for the normalized filter. The presentation follows, to a large extent, the classical exposition of Bain and Crisan's "Fundamentals of Stochastic Filtering", while placing particular emphasis on intuition, motivation, and the interpretation of the main concepts and equations. Many technical arguments are revisited, with additional details and comments intended to facilitate a first reading of the subject. These notes are not intended to be an exhaustive account of stochastic filtering. Their aim is instead to provide a guided tour of some of its central ideas, methods, and equations.