Mathematics for Machine Learning
Imperial College London · View on Coursera ↗
Taught by David Dye, Samuel J. Cooper, Marc Peter Deisenroth, A. Freddie Page
Data ScienceMath and Logic
About the course
Offered by Imperial College London. Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data ... Enroll for free.
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 21,596 ratings. Of those, 3.8% are 1–2★.
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Reviews read on 23 Oct 2026.Read them on Coursera ↗