with great assignments,one could get relation of LA with computer science.For naive english it could hurdles at first.but nice content.must for intermediatory

The material was very well presented, and the exercises were helpful for learning

It is a good course. It mixes theory and practice with Python in a right way and I could learn/review a lot. It has also the right amount of coursework. It is always a pleasure to study using the courses from Coursera. As a lifelong learner I want to be permanently engaged in online courses, from Coursera, EdX ou Youtube. It is the future of Education.

This is a well designed course, with the right balance of theory and practice. The quizzes and programming assignments reinforce the material effectively.

poor presenation

The program is really useful for exploring machine learning. I appreciate that the math involved are so relevant to SVM, even though I am BA major and do not have strong math background. I just need put enough time and go through each example. That's where I find this course very suitable. Plenty examples to explain the concepts.

This is a great courses, sometimes explanations could be better but in general is awesome and they teach us good applications of linear algebra in the field of machine learning. I would like to rate this course with 4.5, but Coursera does not allow us to rate in that way.

The programming assignments were really challenging! I thought I'm not gonna pass this course since it's my first time to encounter Python language. Thank you so much Prof. Piontkovski and Prof. Chernyshev for the new learnings :)

I learnt a lot with this course, good introduction to linear algebra, and good guidance to the use of sk-learn for Machine Learning.

Good, but have few examples and exercises.

Very conventional and theoretical way of presenting the stuff. There are some Python exercises, though.

Not much course materials.