I would love to see an even higher-level video that situates Machine Learning / Neural Networks within the panoply of statistical techniques for data reduction and feature extraction/recognition.\n\nFor example, PCA and SVD represent the apex of a long line of statistical techniques that do their thing without training, that answer the question What can this data tell me about itself?\n\nMachine Learning is at the other extreme, requiring not only extensive training, but also the separation of data into training and validation sets, answering the question How does this data relate to all other data I've seen?  And Machine Learning itself is split into many sub-domains, primarily based on the presence or absence of tagged data to use for learning, but also on the need for time-domain processing (versus single-event processing).\n\nIn between fall low training techniques, such as Gradient Boosting, and perhaps some Bayesian techniques.