Explore linear-algebra learning for vectors, matrices, and systems, with guidance on prerequisites, geometric interpretation, and independent problem solving.
10 learning resources in this collection
Linear algebra connects vectors, matrices, transformations, and systems of equations. Its ideas appear throughout data science, robotics, and engineering. Understanding both the calculations and their geometric meaning makes later applications easier to learn.
Check algebra and calculus expectations for a university course. Work through small examples by hand before relying on software. A matrix operation becomes more useful when you can explain what relationship or transformation it represents.
Solve a small system and interpret the result geometrically. Try a case with no unique solution and explain which property of the system changed.
Coursera · University of Colorado Boulder — James Bird
Colorado Boulder offers a focused linear algebra course covering elimination, matrix properties, eigenvectors, projections and least squares.
Coursera · Howard University — Dennis Davenport
Practice inverses, matrix arithmetic, row reduction and eigenvectors with Howard University’s Python-based linear algebra course on Coursera.
Coursera · The University of Sydney — David Easdown
David Easdown develops geometric vectors, systems, matrices and eigenvectors in Sydney’s substantial introductory linear algebra course.
Coursera · Howard University — Dennis Davenport
Howard University introduces matrices, vector equations and Python notebooks, connecting elementary linear algebra with practical data problems.
Coursera · DeepLearning.AI — Luis Serrano
Luis Serrano connects linear systems, vector transformations and eigenvectors with Python exercises for machine learning and data science.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Study linear systems, vectors and matrix equations with Johns Hopkins, including row reduction and the meaning of existence and uniqueness.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Explore matrix algebra, subspaces, determinants and diagonalization in the second course of Johns Hopkins’ linear algebra sequence.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Johns Hopkins develops orthogonal projections, least squares and symmetric matrices in an intermediate course on geometry and linear algebra.
Coursera · KAIST — Yoon Yong Jin
KAIST links elementary linear algebra with AI applications, covering combinations, inverses, determinants and eigenvalue-based transformations.
MIT OpenCourseWare · Gilbert Strang, Massachusetts Institute of Technology
Gilbert Strang's MIT linear algebra course on systems of equations, vector spaces, matrices, eigenvalues, and geometric interpretation.