<ul><li>Linear algebra is a crucial language behind high-dimensional data, embeddings, PCA, and neural networks in machine learning.</li><li>Understanding linear algebra helps in improving models and debugging them when issues arise.</li><li>Matrices are powerful transformations that act on vectors, enabling rotations, scaling, flipping, or squashing of data.</li><li>Eigenvalues and eigenvectors play a significant role in understanding transformations and finding hidden structures in data for applications like PCA and SVD.</li></ul>

Linear Algebra For Machine Learning — Explained Visually

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