Linear Algebra with NumPy: Matrices, Eigenvalues, SVD & PCA

Why watch

This tutorial covers NumPy's linear algebra module for computing matrix determinants, inverses, and solving systems of linear equations, with emphasis on why numpy.linalg.solve() is preferred over explicit matrix inversion. It explains eigenvalues, eigenvectors, complex eigenvalues from rotation matrices, and demonstrates singular value decomposition (SVD) as a foundation for principal component analysis. The video includes a practical PCA example reducing 5-dimensional data to 3 dimensions while preserving most information, making it ideal for developers and data scientists learning numerical computing.