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.

I​t 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.

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.

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.

Perfect course for newcomers that want to understand basic concepts of Linear Algebra. Very beginner-friendly, especially programming assignments where you get full guidance with the task. Would certainly reccoment to anybody who has interest in this subject, but was too afraid to begin studying it out of complexity.

The course and the content is quite fruitful for anyone who wants to go ahead in the area of Machine Learning. The course instructor gives a detailed understanding of each topic and insight of the methods of vector calculus and linear algebra. For building the basic fundamentals of ML, this course is must for anyone.

It was honestly great. The first two weeks didn't have much new for someone who'd already taken Linear Algebra, but the last three weeks were very informational. It really helped me understand the concepts geometrically/spatially in ways I hadn't seen before when I had taken general linear algebra at my university.

Course content is very useful and intuitive. I definitely feel much more confident with my Linear Algebra.

One thing I would suggest is to provide more exercises / practice quizzes on algebraic manipulation with matrices. I think this would immensely help in following the proofs and building a more solid intuition.

This course brilliantly delivered on each of its intended learning objectives in an engaging and non-threatening way - I would encourage anyone interested in this topic, regardless of their background. The course instructors are excellent, and the forum discussions are extremely helpful if/when you are ever stuck.

The course is great for those who are new to machine learning and want to start from mathematics behind it. The course focuses on vector and matrices and how to solve System of Linear Equations using it. You will develop intuition of what matrix transformations are and how to use change in basis to your advantage.

Superb lectures and lucid explanations of the topics make this course one of my favorites! The video quality was superb and the course content, assignments and degree of difficulty was wonderfully designed to test the skills. Would definitely attend more courses from Imperial college.

Thank you

Dr. Jitesh Tripathi

Great course. Really enjoyed it because the instructors teach well. Also, the practice quizzes are useful for understanding the content. I was able to do all the assignment thanks to all the practice that they have given. Great course and I recommend that anybody interested in machine learning take this course.

Really recommend to all who would like to dive into machine learning with some mathematical background in vectors, matrices and eigenstuff. The instructors are very good and the homework/programming assignments are manageable while giving good insights into the application of the formulas learned in the course.

This is a great course. All explanations and examples are easy and useful, the tasks are challenging but solvable. Certain points of the course might be unclear for students with limited math knowledge, some tasks will make you look for extra info elsewhere. But all in all I would really recommend this course.

Great Course, exceptional in every way, gives you practice drill down some of the concepts, and handy programming assignments that are fun to work with, while not a complete refresher the course is good enough to grasp essence of linear algebra to build intuitive math, rather than classical way of teaching.

It has been a while since I took Linear Algebra in my undergraduate years. This course has improved my knowledge of Linear Algebra and most especially eigen theory. This will greatly enhance my understanding of Machine Learning. Thank you to the professors for imparting their knowledge of Linear Algebra.