This course was very insightful. The instruction was well done with expressing the intuition, but the process was left vague on a few concepts and required me to look up worked out examples.

Adding more examples would have made this course better. I got confused during some assignments because it was the first time I applied the concepts and did not know what I was doing wrong.

I think it is a great course. It is definitely not for beginners and I feel the lectures are somewhat rushed on some videos. It is best to view this course as a supplement and not as a main

good videos and good instructors; programming assigments could be a little bit clearer in the instructions. Overall good for understanding the maths behind Machine Learning. Thank you!

The course was good but it could have been better if the exercises had more difficult questions or probably a section with more difficult questions using the concepts that were taught.

Great course, a lot of visulizations to show you what linear algebra do and why do we need it. The professors really make the course content interesting and the course flow is great.

This is excellent course provided as fundamental skills required in Machine learning journey. I was excited to be learn from best lecturers ever and, thanks to Imperial College.

1.Need more clarity on calculating Eigen vectors using back substitution of Eigen values.

2. Power Iteration method for the Page Rank Algorithm should be more specific and clear.

The course can help me more clearly when approach some algorithms in the optimize function of Machine learning. Thanks coursera and Imperial London College about this course.

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A really well structured course, a few minor problems with the coding assignment formatting but otherwise all the topics were covered in depth with plenty of application tests.

The instructors and the way of their explanation are a huge benefit of this course. The intuition they provided each step in Linear and Vector Algebra are really appreciable.

It's a very good course given that it's so short. It was exactly what I was expecting. I thought it could have had more examples of solving problems with specific techniques.

Very interesting presentation of matrices and vectors. The questions in quizzes could be improved by making them clear. May be you could add another course on eigen analysis.

Topics are explained neatly but lacked in depth explanation in few topics and i suggest to include more application oriented examples to every topics covered in the course.

Good Course, It shows Linear Algebra from different perspective. You should not be good in math because the math is not advanced here, but you should be excellent in Python.

This course is can be challenging but the material is good and i trust this over a university program since this course is applied concepts and works with lite programming.

It really was useful, but need a little bit more concentrated discussion forums. cause many questions were still unanswered there.

but thank you all, it really helped me :)

I think the concepts are explained clearly, with ample examples to real life. Much easier to understand this time around. The coding labs could use more pointers though.

it is quite intuitive, the programing tools are easy to use. I think its perfect for those people who have some math background but don't want to go deeper into theory

The course by no means replace a full semester course on linear algebra, but it´s useful for those who had already had a course on L.A. years ago and want a refresher.

All the lectures were great, and pretty straightforward, but, some programming assestments were too dificult and not so useful in order to help to grasp the concepts.

The course is a good review of linear algebra for machine learning. But It would have been better if there were more code exercises and if they were more challenging.

Brushes over a lot of relevant topics, but if you want something more in-depth, definitely take something on Khan Academy, or your local university/community college.

The video instruction was great but the assignments were difficult to understand given the instruction. Also, the python often failed due to unrecognized routines.