For someone having already graduated with a degree in Mathematics, the mathematical concepts centred around this course were easy to understand, but then applying this knowledge in to code was challenging. Which I was expecting it to be given my lack of experience with python and jupyter notes. A worthwhile course for anyone looking in to data science.

Great content, lecture videos are brilliant. I would make one suggestion; it would be great to have more examples or even recommend text books that we as learners can download or purchase, this will assist those who wants to learn these techniques in practical examples. Other than that I have learned alot and will continue using coursera, good job guys

This is a very good introduction and review of Linear Algebra. The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics. I also learned where I need to learn more in order. Overall will recommend either as a review or a stepping stone to learning more about Linear Algebra.

One of the best Linear Algebra [LA] courses for beginners/novices. It takes away the drudgery of algebra and formulae and tries to explain the "essence" of LA. This is by no means comprehensive LA course - but good enough for people who are fed up with "this is how to calculate the Eigen vector/determinant/<insert pet peeve>" mode of teaching LA.

Brilliant course which helped me to re-learn/learn linear algebra methods for machine learning! The course instructor videos, course structure, worked examples and assessments were all extremely useful and allowed me to achieve my learning goals. I would recommend this course to anyone (but would maybe first suggest brushing up on basic python).

Me parece un curso muy bueno, es básico pero la verdad hay que practicar mucho haciendo ejercicios y no conformarse únicamente con la información de los vídeos, si no, buscar otras fuentes para complementar. Para ser básico fue un desafío porque hay problemas que aparecen en los exámenes que requieren de mucho análisis. Vale la pena; me gustó!

I STRONGLY recomend to take a look to the 3bluebrown series on linear algebra before this course. Don't get me wrong, this course is great for understanding the basics of linear algebra as well as doing some code applying its topics, but a conceptual understanding of what is explained in the course will make it even better and easier to grasp.

If you are completely new to the subject you may find the course a bit challenging at times but after taking several traditional courses on the subject I can say that by far this is the best course on linear algebra I ever took! Well balanced, straight forward, and great intuitive approach, as well as very neat production! Highly recommended!

This course has helped me to understand the basics of linear algebra and it's application in computer science. I was aware of mathematical calculations of the linear algebra, but I did not know reason and meaning of those calculations. I am grateful to Imperial College London and Coursera team for giving me opportunity to take this course.

It is a brilliant course, both the instructors did a great job in making is clear and interesting. This course makes the subject really interesting to learn and gives you really good intuition about operations using linear algebra. The thing I loved about the course is that, it covers only parts of linear algebra that is useful for ML.

I love this course as it is very informative and interactive and made maths more interesting in how it is applied to Technology. Definitely will recommend taking this. Thank you for providing this oppurtunity and thank you to the faculty Mr. Dan Dye and Mr. Sam Cooper for making linear algebra with matrices and vectors more meaningful

Excellent course with the just right amount of detail to expose beginners to the concepts of linear algebra. I look forward to other courses from ICL in coursera in the filed of mathematics, data science and machine learning.

Thanks to this course, I am in love with linear algebra and am continuing further self-study on this subject.

Awesome content and very clear lectures. Would be great to have links to more in-depth explanations of certain unexplained assumptions. For instance, it's unclear how the characteristic equation comes about (by assuming that the characteristic matrix does not have an inverse) and also why the page rank matrix is setup the way it is.

This course helped me having an algebra refresh after about 10 years since the last time I studied at the university. I was looking for a course that helped me focusing in the concepts and I definitely found it. I feel much more confident now about my understanding of determinants, eigenvalues, eigenvectors and matrices in general.

I really appreciate staff of ICL's effort to bring us such an intuitive and straightforward course. It's totally different from those linear algebra courses I've received in China. From your idea on explaining this course on space and transformation, I started to build a strong foundation about linear algebra, and machine learning.

I did the 3 specialization lessons "Mathematics for Machine Learning" (Linear Algebra, Multivariate Calculus, PCA). I really had a lot of fun and learnings in the first one (5 stars for Linear Algebra): David Dye is an increadible teacher. Thank you for your enthousiastic Knowledge Transmission: Mathematics are very cool with you!

This course lays the groundwork for the Algebra required in ML. The basics are covered really well.There are quizzes and assignments to strengthen the ideas learnt in the course.At times felt the assignments are very easy .It can be used to brush up the basic Algebra or learn from Zero. The instructor explains every thing clearly

This course gives a good overview of linear algebra using python numpy arrays. It doesn't go super deep into the topic, but I wouldn't call it superficial. It requires you to do some basic vector and matrix algebra by hand, build agorithms to do some of those calculations, and introduces some numpy methods for those operations.

Excellent course. I have never taken a linear algebra course before, so it took me longer to complete this as I had to learn the basics to follow the material in this course. The course is designed as a review of linear algebra, but if you are motivated and have time, it's possible to complete without having had linear algebra.

This is exactly what I wanted from an online course! I took linear algebra at university decades ago, but made the mistake of learning just enough to pass the next test. The lectures in this course laid out and solidified concepts for me which were previously abstract. The presenters were clear, concise and, I daresay, fun!

This is a good course that allows you to develop a high and low level understanding of linear algebra...unlike a certain university professor I had who made us do 5x5 matrix transformations by hand. I highly recommend doing outside reading alongside the course to expand your knowledge, especially regarding the coding aspects.

Great balance between Mathematical rigor and Computer Science applications. This course is deliberately not easy to ensure you leave with a strong intuition behind the Mathematics of Machine Learning. Python exercises brings this cause alive. It has given me the confidence to continue with my Machine Engineering journey.

Amazing course.Do not let the easy content distract you from the fact that this is one of the best/well taught MOOCs on Coursera.These professors are experts at helping student develop an intuition for mathematics.Way different from what was taught in my school/university and also much more useful in a practical sense.