Explore techniques for determining the appropriate amount of data needed to build effective machine learning models in this 26-minute video by Jeff Heaton. Learn about extrapolation and interpolation in both univariate and multivariate contexts, and understand how to measure data coverage across multiple dimensions. Discover methods for recognizing multimodal distributions, interpreting machine learning curves, and using Mahalanobis distance. Examine a practical example using a diabetes dataset, including feature importance ranking and creating bounding hyper-rectangles. Gain insights into ensuring your training data adequately represents the full range of scenarios your model may encounter in real-world applications.