Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Eigen Space

The video tutorial explains the concept of data matrices, focusing on their structure and the importance of dimensionality reduction. It introduces the idea of subspaces and how the rank of a matrix can be used to determine the dimensionality of these subspaces. The tutorial further explores the concepts of column and row spaces, emphasizing the significance of linearly independent columns and rows. It discusses quick methods for dimensionality reduction using matrix rank and highlights the limitations of this approach, leading to an introduction to Principal Component Analysis (PCA) as a more effective technique for reducing dimensions while maintaining data representation.

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