Explore a detailed lecture on machine learning theory that delves into the concept of shattering and introduces the Vapnik-Chervonenkis (VC) dimension, a fundamental measure of the capacity of a statistical classification algorithm. Learn how this crucial theoretical framework helps understand the complexity and capabilities of learning algorithms, building upon previous discussions of shattering to provide deeper insights into machine learning model evaluation and selection.