Great course! This is exactly how education should be! Give us the intuition to what we are doing, relate it to real world problems and when is this knowledge useful and then get the opportunity to code that knowledge in python instead of wasting time with just hand calculations! Brilliant!

Fantastic course providing a broad understanding of linear algebra for machine learning. The responsive quizzes and formal assessments provide a challenge and regular feedback on performance. Highly recommend taking their course for anyone who wants to develop the maths that underpins ML.

A great course to learn the fundamentals of Linear Algebra for Machine Learning. The programming assignments in Python were the best part of the course because when I studied Algebra at my university I only did boring manual exercises. I recommend this course completely, you'll enjoy it.

This is an incredibly great course for linear algebra. Thank you so much for the neat and elegant explanation! Highly recommend it if you focus more on calculation without knowing the meaning behind matrices and vectors in your past linear algebra journey. Thanks a lot dear professors!!

This course has exceeded my expectations in some ways. I was just trying to get a refresher in basics of Linear Algebra. The intuitive understandings presented in the course were really helpful and gave me a better understanding of the concepts which I only learned mechanically before.

Great course, well presented videos and challenging but engaging content. Great high level view of linear algebra to give you a starting point for other courses. May be useful to have some machine learning knowledge before taking - Andrew Ng's course would serve as a good counterpoint.

This course provides an excellent overview of key linear algebra topics. The instructors do a great job building intuition about matrix transformations and eigenvalues/eigenvectors. Knowledge of python isn't required but will be helpful to do calculations and try out example problems.

After taking this course I can safely say that I did not understand Linear Algebra before. This course introduces basic concepts useful for machine learning and it gives a very intuitive view on abstract concepts that I had trouble understanding before. I would totally recommend it.

Great introductory course. Linear Algebra is quite often the most poorly taught/understood subject in college mathematics.This course has a done a great job in stressing on the core concepts without focusing on the computational details which happens in typical linear algebra courses

It's been many years since I've reviewed these topics and this was as interesting and as painless as could have ever hoped. Production quality was top-notch and so was the teaching, felt engaged the entire time. I'm really looking forward to completing the rest of the specialization.

A great course with a lot of applications and visual explanations of basis, matrices etc. I really liked the approach used by the profs, explaining why we need to learn this rather than giving a bunch of equations and leaving us to figure out rest ( the way they teach in schools ).

Very much recommend this course for absolute beginners seeking to refresh/learn math required for machine learning.

Don't be afraid to start and focus on learning instead of going through the material.

Practice exercise you've done several times and return to your notes. Good luck!

Great course! Linear algebra is a very vast subject. This course helped me getting the idea of topics I need in machine learning algorithms. This course is very helpful in revisiting the linear algebra to those who have taken this subject in his/her college in very short time.

Excellent course. I was nervous starting the course, as I can find maths challenging, but I actually really enjoyed it and it has given me more confidence. In this course there is a focus on understanding what is being done and its applications, which is exactly what I wanted.

It's a worth-taking course. But you'd better have some linear algebra background. Like me, a student in China, we learn all things with out geometric insight, it will be very difficult for you to take the course through out.

All in all, worth-taking. Give me many fresh airs.

I actually studied Maths at undergrad and was using this as a catchup after many years - it wasn't taught nearly anywhere near as well as this. More lecturers should focus on the concepts first, and then the formulae to give context. A great course, highly recommended!

Well-paced, engaging and highly interesting course content. This course totally gives a new dimension to linear algebra. The fact that mathematical examples are implemented through programming exercises, really strengthens the concepts and makes it even more interesting.

I would like to thank the instructors for their exceptional work. They are teaching mathematics with the aid of visualizations, which is not common within ordinary math classes. This way assists students to understand the physical interpretation of mathematical concepts.

It is quite hard course, especially coding.

the practice tests are very useful. Every test provides description which is very useful to review the lecture. Tests are challenging but if we make effort and invest time to think, read the instruction carefully, we can pass.

Lectures are well-paced (although I was familiar with basics of working with vectors and matrices from high school mathematics). The assignments and quizzes were pitched at the right difficulty, just hard enough to be a challenge but not so hard as to be disheartening.

pretty nice course which contains linear algebra for machine learning. learned alot and had a lot of fun during the course and assignments. python assignments were great and challenging, final exam was so challenging. special thanks to instructors and Imperial College

R​eally good course to deep dive into Linear Algebra for ML. The course is fast paced, but you get plenty of opportunities to practise. A Python programming component is present, in which you translate the mathematical working into a computer program. Tough, but good.

Eigenvalue &Eigenvector, Matrix & Inverse Matrix, The Gram–Schmidt process, Page RanK.

I was weak in maths and my background was not that strong, But I learned here how to tackle with

wonderful lecture tutorials

I want to apply ML in my research in electric power system

What an excellent lecturer.

I just wish that my mathematics teacher at school had had a tenth of the ability to impart knowledge.

This is a fantastic course and I will be doing the specialization later when I get some free time.

Thank you for a fantastic course.

Dariusz