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Back to Mathematics for Machine Learning: Linear Algebra

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

4.7
stars
11,930 ratings

About the Course

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

Top reviews

EC

Sep 9, 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

PL

Aug 25, 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

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1801 - 1825 of 2,363 Reviews for Mathematics for Machine Learning: Linear Algebra

By Vedhasankaran H

Dec 31, 2020

Excellent Course Content and well presented. The instructors did a great Job in conveying the fine details . However The course should emphasize Python as a prerequisite and the instructors can include a lecture on "Basics on Linear Algebra with Python " in this course as the Assignments from Week3 through 5 involves applying Linear Algebra in Python

By Julio V

Sep 27, 2018

I feel like some part should've gone a bit more in depth. Due to time constraints for the course, I guess that's why some topics where not developed further. Would be quite nice in these cases if you could point to other sources, books, etc. Or maybe do a compilation of sources based on what the students have used to get unstuck on particular issues.

By Régis M

Dec 28, 2018

As paletras e numero de exercios foram muito bons. Porem o forum não é muito bom, existe questões abertas a 4 meses que ainda não foram respondidas, e muita repetição de duvidas.

Poderia ter apos os exercicios praticos, um video explicação de como resolver. Porque se a media é 80%, é presumivel que o aluno pode não saber alguma coisa e ainda passar

By rakesh c c

Oct 22, 2018

I loved doing this course. I did this course to revisit the concepts I have learned in my undergraduate, I remember most concepts but there are few moments where I have to watch videos again and again to follow along, anyone who is beginner might find it a bit intimidating, but don't give up just follow along and connect the dots between concepts.

By Matteo L

Apr 20, 2020

I think this is a great review of linear algebra, especially for someone who has already previously studied the topics.

The example with the PageRank algorithm was very interesting and a great add to the course.

Possibly a downside of the course was a lack of practice of the material, especially considering how easy the notebook assignments are.

By Carolyn O

Feb 26, 2021

Goal is to get a gut feel through understanding math behind the python functions, but I wish they had start doing the code in parallel sooner. At end of the course, they accomplish this. Hand calculations in middle weeks were long enough to distract from the overview. It says its beginner course, but glad I had some background.

By Yazhini P

Jan 26, 2020

The course and the faculty were amazing altogether. All my queries regarding linear algebra were cleared and I began to look at linear algebra in a new eye.

The only flaw was inaccessibility to the correct Notebook link. Only after going through the forum was I able to get the correct link as it was, luckily, posted by someone.

By Emmanuel G

Apr 14, 2021

Covers some good basics, but I feel that I would have struggled with the programming assignments if I didn't already have some practical experience with data science in Python and linear algebra. In particular, the last 20% of the course felt (eigensystems) felt rushed and could have been expanded upon a bit more thoroughly.

By Vinayak N

Oct 14, 2018

Good for starters. It gives a holistic view of linear Algebra. Geometric interpretation of Eigen Vectors was the highlight of the course for me as I wasn't aware of it before and the instructor helped me understand the concept very well! Thanks for putting forth this course and hope to see more in the forthcoming sessions :)

By Rick M

Jul 21, 2019

Overall, I thought this course was worth the time. Some of the material was challenging, but the instructors were pretty good at explaining clearly. Just a head's up: there is relatively little reading material here, so if you struggle to learn through videos you might have a hard time. That part was a challenge for me.

By Henri S

Oct 9, 2020

Could be nice to have the complete mathematical definitions given in an annex for those that are interested in refreshing their maths more than understanding the concepts broadly throughout the examples. Otherwise very well taught, I like that there are many examples where you have to get back to basic calculations.

By Simon W

Jun 27, 2020

Good course overall and I enjoyed the top-down approach in instruction, which helped me understand the big picture before proceeding to do specific linear algebra computations. However, I wish there were more lecture contents and exercises to help me build a better foundation and clear up occasional confusions.

By RICHARD A (

Jun 6, 2020

The course already cover all some of essential topics in linear algebra is is a good course to refresh linear algebra and get hands on coding on how we can use linear algebra for computation. I would be great if the course also covers other essential topics such as null space, column space, pre-image, and image

By Subham K S

Jan 30, 2020

Great course!! The instructors taught in a great way with proper visualization and real-world applications.

But more examples of implementing in machine learning could have been included and a bit more concepts could have been taught.

Overall great one. Thank you coursera, Imperial college and both instructors.

By Beyza A

May 3, 2020

I have 2 years of experience with coding. I took this course to refresh my knowledge of mathematics before I start using machine learning techniques. This course sometimes gave us the basic knowledge which helped to apply real-world situations. However, I feel like I need more exercises, basic explanations.

By Oriane N

May 3, 2022

Very well explained with videos and a recap PDF. Guided exercices to practice with manual calculations and computer programming (Python notebooks) and questions to get the intuition of what's going on with special cases. I recommend and will continue the specialization with the other Maths for ML courses !

By Sandeep M

Apr 30, 2022

I really enjoyed the course. Great learning experience. There's one area where I felt that the course could have done better. And that is explaining the interpretations of various mathematical calculations. These interpretations were embedded in the quizzes and the assignments. But they were very cryptic.

By Luis F H

Dec 10, 2020

The videos and materials are great, departing from zero in the subject I was capable of understanding and practicing., but some programing exercises demand any knowledge in python, what makes things more difficult in a few moments. Would recommend for anyone that wants to enter into the ML world.

By Joshua P

Dec 30, 2023

It is a good course but it is annoying when the quizzes jump into concepts that werent described in the videos, though I understand they are trying to make it intuitive, it can be very frustrating. I definitely have a much larger understanding of linear algebra and Python than when I started.

By Chip B

May 25, 2019

Filled in a lot of knowledge gaps that I should have learned in high school or undergrad. I feel much more prepared for graduate studies in data science.

4 stars because the last module felt rushed. I felt that I learned more from trial and error on the quiz than from the lecture videos.

By Kara

Jul 4, 2020

The content is good, and I can see that the instructors are trying to let students understand the mechanism behind the calculations. However, the lectures are too short for students to fully understand everything. I would suggest to extend the length of the videos and provide more details.

By Frank G

Apr 14, 2018

Very good class. Outstanding instructors very clearly teaching key concepts in linear algebra.

I only docked one star for two reasons:

I wish they explained in more depth how the linear algebra topics are used in machine learning.

I wish the class were a little longer and more in-depth.

By Sagar

Oct 23, 2020

Mathematics is the core of machine learning. This course is best for understanding the mathematics of machine learning. The course was in-depth and intuitive. The assignments were a bit difficult for the new programmers. But overall, the theory classes were clear and understandable.

By Sydney F

Jul 26, 2019

While they explain the basic concepts of linear algebra, sometimes the programming assignments are tricky and some of the quizzes are too complicated to complete with our current knowledge. However, the course is worth taking if you want a solid math background for machine learning.

By Saurabh P

Mar 5, 2019

the lectures were very good and on point, obviously referring the prescribed textbooks will further improve one's knowledge about the subject. i really enjoyed the programming part of the assignments, which were made to help students without any prior experience of python language.