Drag vectors to see a span collapse, warp the grid with a matrix, hunt eigenvectors by hand, run Gram–Schmidt step by step, and watch a circle become an ellipse under the SVD.
Linear algebra is the language of machine learning, graphics and data science, and most of it is easier to see than to read. The Linear Algebra Lab turns each idea into something you can drag: two vectors and the plane they span, a grid that a matrix stretches and shears, an eigenvector that stays on its own line while everything else turns.
Every experiment pairs a picture with the numbers behind it, and a short theory note underneath, so you can check your intuition against the definition. Matrices can be typed in or picked from presets, and the lab reports the rank, determinant, eigenvalues and other quantities live as you change them. It works on a phone as well as on a computer.
It suits first-year engineering and science students, anyone revising for exams such as GATE, and people moving into machine learning who want the geometry behind PCA, least squares and the SVD. The experiments run roughly in the order a university course teaches them, from span and elimination through eigenvalues to the SVD.