Introduces the fundamental concepts of linear algebra through a rigorous and conceptual approach that emphasizes linear transformations and vector spaces before matrix computations. Covers vector spaces, linear maps, matrices, determinants, eigenvalues, eigenvectors, invariant subspaces, inner-product spaces, operators on complex vector spaces, and spectral theory. Designed for undergraduate and graduate students in mathematics, computer science, engineering, physics, data science, machine learning, and related disciplines.