Machine learning, LLMs, RSA, etc.
It's generally useful for multivariate statistics, 3D flies (insects), in 3D space, clustering about a narrow slanting plane of light from a window slit are points that can be projected onto "the plane of best fit" - nominally the slanting plane of light.
That right there is a geometric picture of fitting a line, a plane, a lower order manifold, to a higher order data set, the errors (distance from plane), etc. and something of what Singular Value Decomposition is about (used for image enhancement, sharpening fuzzy data, etc).
The real test of applications is what kind of work do you see yourself doing? - A quick back read suggests your currently a CS student, so all unfocused potential for now (perhaps).
Linear transforms (such as rotations and displacements) in GPU graphics.
Fourier series in signal processing.
JPEG compression.
Obtaining the best fit element in a vector space of curves given data or other constraints.
Understanding autodiff in JAX.
The mathematical definition of a tensor helps develop intuition for manipulating arrays/tensors in array libraries.
Transition matrices of a Markov chain.
PageRank.