Linear Algebra - Khan Academy
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Learn linear algebra fundamentals, including matrices, vectors, and transformations, with Khan Academy's comprehensive course.
More resources on Matrices
Wolfram MathWorld
MathWorld is an online mathematics encyclopedia from Wolfram Research offering detailed, browsable articles on topics across the math spectrum, including algebra, geometry, calculus, and number theory. Each entry includes definitions, theorems, formulas, diagrams, worked examples, and links to further reading.
Eigenvectors and eigenvalues
Chapter 14 of 3Blue1Brown's Essence of Linear Algebra series. Grant Sanderson animates eigenvectors as the directions a transformation only stretches, then derives the characteristic equation and eigenbases, giving viewers geometric intuition behind the usual determinant-based computation and diagonalization.
3Blue1Brown Linear Algebra Series
Grant Sanderson's animated series on the geometric meaning of linear algebra: vectors, linear transformations, matrix multiplication, determinants, eigenvectors and abstract vector spaces. Viewers come away able to picture what a matrix does to space, which makes later computational courses far easier to follow.
Linear Algebra (MIT 18.06SC)
Matrix theory and linear algebra following Strang's textbook: elimination, vector spaces, orthogonality and least squares, determinants, eigenvalues, positive definite matrices and the singular value decomposition. Includes 74 lecture and problem-solving videos, summary notes, and problem sets and exams with solutions, built for independent study.
Essence of Linear Algebra
Grasp determinants with 3Blue1Brown's "Essence of Linear Algebra" course. Visualize and understand this key linear algebra concept.
Linear Algebra (MIT 18.06)
Matrix theory and linear algebra: systems of equations, elimination, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and positive definite matrices. Includes 35 lecture videos plus problem sets and exams with solutions, giving the fluency needed for applied mathematics, engineering and data science.