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.

.

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.

Excellent introduction to linear algebra fundamentals, even with little/no prior exposure. Familiarity with programming concepts makes later exercises much easier.

It is a good course for beginners and to revise the concept. I feel the lack of explanation in some concepts which could have been avoided by providing references.

Short and fast crash course for brushing up linear algebra. Its better to take Khan academy tutorials and then start this to finish fast and understand the logics.

It was a great hands-on experience with Linear Algebra but sometimes parts of the course got difficult, I couldn't get any of the tips given by the professionals.

Found this course very interesting, as it has good concepts regarding use of applied mathematics in machine learning. Looking forward for more interesting courses

the course is good, there should be more detailed explanation of the topic, the assignments are good and keep our learning at top notch. Thank you so much team.