Bayesian Learning is a statistical approach that updates the probability of a hypothesis as more evidence or information becomes available. It is based on Bayes' Theorem, which describes how to calculate the likelihood of an event based on prior knowledge and new data. This method allows for continuous learning and adaptation, making it useful in various fields such as machine learning, data science, and artificial intelligence. In Bayesian Learning, prior beliefs about a model are combined with observed data to form a posterior belief. This process helps in making predictions and decisions under uncertainty, as it quantifies the uncertainty associated with different outcomes. By incorporating prior knowledge, Bayesian Learning can improve the accuracy of predictions over time.