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Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London

4.7
stars
9,600 ratings
1,938 reviews

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-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

Top reviews

PL
Aug 25, 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

NS
Dec 22, 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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1851 - 1875 of 1,930 Reviews for Mathematics for Machine Learning: Linear Algebra

By Carlos R T G R

Mar 18, 2019

The videos need to be updated, there are quite some errors that are already identified...

By Roberto V

Apr 12, 2021

Lack of feedback or more help from instructors to doubts, but the course is very good.

By rishabh t

May 5, 2020

Explainations was good but some topics was difficult to get may be due to my basics

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 Tory M

Sep 3, 2020

All in all this course served as a good refresher for linear algebra.

By Gary M F T

Oct 29, 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 25, 2020

The course is good but the explanations are not clear enough.

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 Rafael L A

Jul 9, 2020

challenging

By Navya V

Jul 18, 2020

good

By Amit V

Sep 8, 2020

1.) This is definitely not a course for beginners, especially if one does not know how to code OR if he/ she is weak in coding.

2.) As far as lectures are concerned, the faculty members/ lecturers are energetic. While some topics have been explained really well, many topics are either left without much explanation. There are some occasional mistakes on the part of faculty, which must've been edited and rectified. They have done good job in converting the lectures in to text. However, there were some mistakes in those texts too.

3.) There is no support in discussion forums from the lecturers of this course. I have seen many questions remain unanswered for many months. This is a very big drawback.

4.) There is a huge gap between what is being taught in videos and what is being asked in assignments. We can understand this by the following corollary: In the video tutorial one teacher is showing that 1 + 2 = 3. In the assignment, students are being asked to find the roots of a quadratic equation.

5.) Some questions and even their answers too technical to be understood by many students. The attempt to explain after the completion of assignment is also too technical. There should be an attempt to dive deeper to help weaker students. If time is the constraint, then make another basic course and let that be a prerequisite of this course. But please, do not mention in the introduction of this course that there is no prerequisite.

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.