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Learn fundamental concepts of distance metrics used in data science through this 80-minute lecture covering metric distances and their mathematical properties, explore L_p distance families including Euclidean and Manhattan distances, understand cosine and angular distance measures for high-dimensional data, discover Mahalanobis distances for handling correlated variables, and examine practical modeling applications of these distance measures in data analysis and machine learning contexts.
