Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London
About the Course
Top reviews
DV
Jun 24, 2019
This was a terrific course; the instructors' are passionate and knowledgeable about the course material, the assignments are engaging and relevant, and the length of the videos feels "just right".
AS
Jul 11, 2019
It's a nice course but instructors should go in more details. It's mostly high school mathematics. I was expecting undergraduate level Linear Algebra. Otherwise it was a good learning experience.
2376 - 2400 of 2,480 Reviews for Mathematics for Machine Learning: Linear Algebra
By Adam R
•Nov 16, 2018
Some of the quizzes go beyond what is in the videos and often spent ages on them.
By Nicholas K
•Apr 20, 2018
Enough gaps that I finished feeling like I really had no idea what was going on.
By David R M
•Jul 13, 2020
Requires an understanding of python that doesn't seem to be expressed anywhere
By Jose H C
•Dec 19, 2019
I did not see any specific application of what was learned to Machine Learning
By Thomas K
•May 4, 2022
Covered topics sup up useful framework that give robust starting point
By Tory M
•Sep 3, 2020
All in all this course served as a good refresher for linear algebra.
By Gary M F T
•Oct 28, 2020
Esta en el idioma inglés. Seria factibles en el idioma español
By Alejandro T R
•Aug 2, 2020
Really difficult to understand the explanations of the course.
By Ayala A
•Jul 24, 2020
The course is good but the explanations are not clear enough.
By Akshat B
•May 18, 2023
The content is good, but the support could have been better.
By Ninder J
•Jun 17, 2019
not well explained...Rather than this go for khan's academy
By rajiv k K
•Jul 21, 2019
Good for rivision but I will not recommend to beginner.
By omri s
•Oct 25, 2019
Good, but a lot of stuff is not explained in detail
By สิทธิพร แ
•May 29, 2020
some lessons don't cover knowledge for assignment
By Flávio H P d O
•May 11, 2018
explanation not very clear
not enought examples
By Rosana J B
•Mar 1, 2021
muy confuso el sistema de envío de tareas
By Hiralal P
•May 4, 2020
they should provide more examples
By Neha K
•Oct 9, 2018
The style of teaching is great.
By Lieu Z H
•Jul 25, 2019
found the course too basic
By Jadhav J N J
•Mar 2, 2020
Good Teaching
By Néstor E S
•Sep 1, 2025
no es bueno
By Rafael L A
•Jul 9, 2020
challenging
By Navya V
•Jul 18, 2020
good
By Sakshi T
•Jan 28, 2023
NA
By Fuad E
•May 22, 2019
It is a little messy: there are no clear definitions of Vector Space, Normed Vector Space, Euclidean Vector Space. Functions as COS and SIN are used to show basic concepts, orthogonal base, and so on. "Projection" concept always relies on base being orthogonal, projection being under 90 degree (what is 90 degree in vector space?), and space being Euclidean, although it is much simpler and applicable for just Vector Space (space without "norm" defined). Good introductory course for high-school; bad for University. Good for kids who just finished learning Pythagoras Theorem, SIN, COS, and basis of Euclidean geometry. Example of house (with number of rooms which is positive Integer number, and price which is positive Decimal) is not really a vector. Examples of non-Euclidean spaces and their applications in machine learning not provided (geometrical deep learning on graphs for example). Basic course for those completely unfamiliar with what "vector" is. Provided tests in Python are confusing because in the context we write vectors (and "base" vectors which matrix consists from) vertically, and in Python - horizontally. For example, [[1,2],[3,4]] is matrix, but it won't transform base vector [1,0] into [1,2]. This is confusing and should be mentioned before test begins.
Thank you for helping me to recall this knowledge. I finished three weeks; I may need to update review later.