Mathematics for Machine Learning: Linear Algebra
Imperial College London · View on Coursera ↗
Taught by David Dye, Samuel J. Cooper, A. Freddie Page
Data ScienceMath and Logic
About the course
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-
The dissent
The most-upvoted critical review (212 votes, 1.0★).
“This course is not suited for beginners and people looking for an introductory lecture to Linear Algebra! Unfortunately the topics are extremely hastily presented and lack depth of explanation, sufficient examples and often leave out content required to complete the assignments. The material is not presented in a coherent way, and, for someone new to Linear Algebra like me, requires a great amount of self-study outside the course (e.g. Khan Academy) to comprehend the content. The team of lecturers is very likeable and enthusiastic. However, I do not comprehend where this course seeks to position itself: it is not suited for students new to Linear Algebra, and, not extensive enough for someone seeking to learn underlying mathematics for Machine Learning as this course simply doesn't cover Machine Learning. Finishing this course, I have some vague understanding of certain concepts and I am left longing for proper and structured content that I could feel confident about.”
Coursera's 4.6 comes from 12,618 ratings. Of those, 2.5% are 1–2★.
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Reviews read on 23 Oct 2026.Read them on Coursera ↗