Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London

I had no prior knowledge of this course. I knew about mathematical narratives, but it was certainly very instructive for me in the ways of interpretation and application contained in his narrative in the course. It is also a basic building block in Machine Learning. I'll definitely sign up for a continuation of the course. I recommend it to everyone. I hope you enjoy it is in you.

I've only done half of the course but I already know this course is one of the best on Coursera! Complex concepts in mathematics are broken down into simple terms. The professor also clearly stated what those concepts are used for in practical, which certainly help learners have a clear idea of why they are learning this course. Very exciting every time I click onto new lessons!

Not being from a Mathematics Background, one surely need best tutor I guess for understanding Mathematics that's required in Machine Learning/ Data Science. Both the tutor Sam Cooper and David Dye amazingly Explained the topics and I'm happy to have completed the Linear Algerbra Course and now moving towards other part of the course i.e Course 2 Multivariate Calculus.

Excellent Course, I remembered the linear algebra that I saw in school more than 26 years ago (I studied applied mathematics and switched to Actuaria), but now with examples related to DataScience.

As observation.

For someone who has not programmed in some language the exercises can be challenging, but they are always very intuitive if the example steps are performed.

Getting back into math after taking about 12 years off, and this was a great dive back in. I got a lot out of working the math out by hand for a few examples. There were some gaps in my understanding (when calculating eigenvectors, we need to solve for x1 and x2, but they don't have to be 0). Overall it was a great course and I'll be referring to my notes regularly!

At first, I was kinda of afraid of Math in general and Linear algebra in particular, but after taking this course, I am satisfied with it.

A special thanks to the instructors and all the people behind this course, for making thing simple and comprehensible, and at the same time, hit the target. Looking forward to keep learning and carry on with this specialization !

This course was very informative . Having learnt to solve most of this problems by hand in under-graduation , this course helped me to code these hand-worked problems . Additionally I was able to understand and visualize what the problems actually do . I highly recommend this course for anyone who is looking to learn or advance their career in machine learning .

This course was of high quality, was very helpful in explaining some key concepts and I appreciated the instructors energy and humor. My only complaint about the course is that some of the quizzes and homework assignments felt significantly more challenging than what was covered in the lessons; however, the discussion forums proved helpful in closing this gap.

Really good overview and while explained perfectly by the instructors (using different media that would have been amazing to have back in school...) still challenging enough to get the brain cells running. Fun to do, yet one should take time and really concentrate. Thanks for this amazing opportunity! I'm sure this knowledge will really help me along the way.

This is an exceptional course for learning Linear Algebra in an intuitive way. i would recommend this course to everyone who is fond of mathematics. This course will also have programming assignments with python and numpy packages. Overall I had a wonderful experience and a handful of knowledge. Thank you for the course creators and professors and lecturers.

For someone having already graduated with a degree in Mathematics, the mathematical concepts centred around this course were easy to understand, but then applying this knowledge in to code was challenging. Which I was expecting it to be given my lack of experience with python and jupyter notes. A worthwhile course for anyone looking in to data science.

Great content, lecture videos are brilliant. I would make one suggestion; it would be great to have more examples or even recommend text books that we as learners can download or purchase, this will assist those who wants to learn these techniques in practical examples. Other than that I have learned alot and will continue using coursera, good job guys

This is a very good introduction and review of Linear Algebra. The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics. I also learned where I need to learn more in order. Overall will recommend either as a review or a stepping stone to learning more about Linear Algebra.

One of the best Linear Algebra [LA] courses for beginners/novices. It takes away the drudgery of algebra and formulae and tries to explain the "essence" of LA. This is by no means comprehensive LA course - but good enough for people who are fed up with "this is how to calculate the Eigen vector/determinant/<insert pet peeve>" mode of teaching LA.

Brilliant course which helped me to re-learn/learn linear algebra methods for machine learning! The course instructor videos, course structure, worked examples and assessments were all extremely useful and allowed me to achieve my learning goals. I would recommend this course to anyone (but would maybe first suggest brushing up on basic python).

Me parece un curso muy bueno, es básico pero la verdad hay que practicar mucho haciendo ejercicios y no conformarse únicamente con la información de los vídeos, si no, buscar otras fuentes para complementar. Para ser básico fue un desafío porque hay problemas que aparecen en los exámenes que requieren de mucho análisis. Vale la pena; me gustó!

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.

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.