Intermediate 34 hours English None CertificateFREE
Editor's Pick
Four million learners can't be wrong — Strang's lectures are the gold standard for linear algebra, the math that ML is built on.
About this course
MIT 18.06 with Gilbert Strang is the most-watched university math course in history — Strang's lectures have been viewed by an estimated 4 million learners worldwide, and his textbook defines how linear algebra is taught at the undergraduate level globally. The course covers vector spaces, matrix operations, systems of linear equations, determinants, eigenvalues and eigenvectors, the four fundamental subspaces, and the singular value decomposition (SVD).
Linear algebra is the mathematical foundation beneath machine learning and data science — principal component analysis, neural network weight matrices, recommendation systems, and many more techniques depend on it. This is the primary reason a course from the mathematics department belongs in a data science and AI catalog: learners who understand the math understand why these techniques work, not just how to apply them.
What you'll learn
Solve systems of linear equations using elimination and matrix factorization
Understand vector spaces, subspaces, and the four fundamental subspaces
Compute and interpret eigenvalues and eigenvectors
Apply the singular value decomposition (SVD) to data applications
Understand the mathematical foundations of principal component analysis
This course includes
34h
On-demand video
Yes
Mobile access
English
Language
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Instructor
GS
Gilbert Strang
MIT OpenCourseWare instructor
4M+ learners6 courses4.9 instructor rating
Taught by Gilbert Strang, Professor of Mathematics at MIT and author of the standard linear algebra textbook used at universities worldwide. Strang is a MacVicar Faculty Fellow for teaching excellence.
Requirements
Basic calculus and algebra comfort helpful
No prior linear algebra required
Who this course is for
Data science and ML students who want the mathematical foundations of their field
Computer science students taking a formal linear algebra course
Self-taught ML practitioners who want to understand why the math works
About this provider
MO
MIT OpenCourseWare
MIT OpenCourseWare — free, openly licensed course materials from MIT's actual courses, including lecture notes, problem sets, and exams. No certificate.
For applying ML tools — no. For understanding why they work, implementing them from scratch, or reading research papers — yes. This course gives you the foundation that applied ML frameworks assume.
Strang recorded updated lectures ('A 2020 Vision of Linear Algebra') also available on OCW — the 18.06 lectures remain the most comprehensive, but the 2020 version is more concise if you want a shorter introduction.
No certificate from OCW — this is the freely available course materials with no credential. Paid certificate options exist through edX for MIT linear algebra content.