I have experience with python numpy and pandas, so I found the assignments reasonable, but i think the programming assignments can be confusing to someone who has never coded before. There're a few times when I found myself lost in the instructor's explanation and needed to find more clarification online, but overall the videos are of great quality. It's a great course for self learning!

The biggest problem of this course is that dot-products are introduced before linear transfomations. I understood dot products through 3blue1brown videos and they are more intuitively explained as the product of the lengths of the projection and the vector projected onto. It is a subset of linear transformation a matrix vector multiplication where one of vectors is transposed.

I'm sad, because I finished the course, but instead of a solid understanding of linear algebra, I mostly feel confusion and frustration from what I was not able to ask the professors. But overall, the instructional videos are high quality, and the quizzes were challenging. At least it forced me to think critically about the subject, so I don't think it was a waste of my time.

The course generally is good. However I think there are some problems in this course: 1. The course pace is too fast, some concepts are hard to understand with few minutes lecture 2. The after-practice didn't help me to boost my understanding to the lecture. Even after I finished the practice, I still wonder why this happens 3. The final quiz has too much calculations.

I was expecting a lot from the course. But, it covers only the very basic portions. For example, I am not sure if I can start understanding the difficulties with normal equations and portions of linear algebra based on calculus. Also, it does not discuss parallelism of ML with linear algebra. I am not sure if this will help me read and understand Andrew Ng's ML papers.

The course content was spot-on, covering some of the most important basics for math in machine learning. I wish there were more programming exercise based assignments and less hand-calculation based quizzes to make it close to real world applications. Overall, loved this course and highly recommend it to data science enthusiasts taking baby steps towards deep learning.

This course has provided everything that it had promised. The professors of this course are really knowledgeable about the topics and the use of real life examples by them to explain each concept proves really helpful. Overall, this course would be a really good starting point for anyone willing to start their journey in the world of Machine Learning and Data Science.

Great course, but difficulty spikes after the first few weeks and problems become much more challenging, albeit far more interesting though. The course reaches its peak when it challenges you on the interesting problems presented to you during the last few weeks. You may need to have some basic understanding of algebra and a bit of calculus too before starting.

Really Good course for people having some basic linear algebra knowledge. They could have done better on explaining some concepts rather than rushing through it... But a great refresher course... One recommendation would be to suggest good books for Linear Algebra that might be helpful for students who take this course, some book which can accompany this course.

Excellent Course Content and well presented. The instructors did a great Job in conveying the fine details . However The course should emphasize Python as a prerequisite and the instructors can include a lecture on "Basics on Linear Algebra with Python " in this course as the Assignments from Week3 through 5 involves applying Linear Algebra in Python

I feel like some part should've gone a bit more in depth. Due to time constraints for the course, I guess that's why some topics where not developed further. Would be quite nice in these cases if you could point to other sources, books, etc. Or maybe do a compilation of sources based on what the students have used to get unstuck on particular issues.

As paletras e numero de exercios foram muito bons. Porem o forum não é muito bom, existe questões abertas a 4 meses que ainda não foram respondidas, e muita repetição de duvidas.

Poderia ter apos os exercicios praticos, um video explicação de como resolver. Porque se a media é 80%, é presumivel que o aluno pode não saber alguma coisa e ainda passar

I loved doing this course. I did this course to revisit the concepts I have learned in my undergraduate, I remember most concepts but there are few moments where I have to watch videos again and again to follow along, anyone who is beginner might find it a bit intimidating, but don't give up just follow along and connect the dots between concepts.

I think this is a great review of linear algebra, especially for someone who has already previously studied the topics.

The example with the PageRank algorithm was very interesting and a great add to the course.

Possibly a downside of the course was a lack of practice of the material, especially considering how easy the notebook assignments are.

Goal is to get a gut feel through understanding math behind the python functions, but I wish they had start doing the code in parallel sooner. At end of the course, they accomplish this. Hand calculations in middle weeks were long enough to distract from the overview. It says its beginner course, but glad I had some background.

The course and the faculty were amazing altogether. All my queries regarding linear algebra were cleared and I began to look at linear algebra in a new eye.

The only flaw was inaccessibility to the correct Notebook link. Only after going through the forum was I able to get the correct link as it was, luckily, posted by someone.

Covers some good basics, but I feel that I would have struggled with the programming assignments if I didn't already have some practical experience with data science in Python and linear algebra. In particular, the last 20% of the course felt (eigensystems) felt rushed and could have been expanded upon a bit more thoroughly.

Good for starters. It gives a holistic view of linear Algebra. Geometric interpretation of Eigen Vectors was the highlight of the course for me as I wasn't aware of it before and the instructor helped me understand the concept very well! Thanks for putting forth this course and hope to see more in the forthcoming sessions :)

Overall, I thought this course was worth the time. Some of the material was challenging, but the instructors were pretty good at explaining clearly. Just a head's up: there is relatively little reading material here, so if you struggle to learn through videos you might have a hard time. That part was a challenge for me.

Could be nice to have the complete mathematical definitions given in an annex for those that are interested in refreshing their maths more than understanding the concepts broadly throughout the examples. Otherwise very well taught, I like that there are many examples where you have to get back to basic calculations.

Good course overall and I enjoyed the top-down approach in instruction, which helped me understand the big picture before proceeding to do specific linear algebra computations. However, I wish there were more lecture contents and exercises to help me build a better foundation and clear up occasional confusions.

The course already cover all some of essential topics in linear algebra is is a good course to refresh linear algebra and get hands on coding on how we can use linear algebra for computation. I would be great if the course also covers other essential topics such as null space, column space, pre-image, and image

Great course!! The instructors taught in a great way with proper visualization and real-world applications.

But more examples of implementing in machine learning could have been included and a bit more concepts could have been taught.

Overall great one. Thank you coursera, Imperial college and both instructors.

I have 2 years of experience with coding. I took this course to refresh my knowledge of mathematics before I start using machine learning techniques. This course sometimes gave us the basic knowledge which helped to apply real-world situations. However, I feel like I need more exercises, basic explanations.

Very well explained with videos and a recap PDF. Guided exercices to practice with manual calculations and computer programming (Python notebooks) and questions to get the intuition of what's going on with special cases. I recommend and will continue the specialization with the other Maths for ML courses !