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

4.7
stars
9,902 ratings
1,991 reviews

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

NS
Dec 22, 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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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251 - 275 of 1,988 Reviews for Mathematics for Machine Learning: Linear Algebra

By Abdul-Rashid B

Jan 6, 2021

Great lecturers, excellent delivery of subject matter. This course did not disappoint me. It provides a concise yet in-depth revision of linear algebra as is relevant to machine learning. Looking forward to more from these instructors.

By dhiraj b

Apr 21, 2020

Offered the much needed perspective of linear algebra to develop actual understanding, than just solving problems without understanding why and how actual computation works. I would like to thank the professors for such a great course.

By Duraivelu K

Apr 11, 2020

This course not only provided me the fundamental knowledge of Mathematics required to learn my next interested course of Machine Learning, but also helped me to kill the lockdown period due to covid-19 pandemic in a useful way at home.

By AKSHAT M

Jul 19, 2020

Excellent course. Outstanding methodology. Great fun and intuition based leaning, kudos to David Dye, Sam Cooper and the ICL team. Thank you very much for bringing forward this course. Looking forward for many more courses from ICL :D

By Greg E

Jul 15, 2019

I thoroughly enjoyed this course. After using matrices and vectors for decades in my work, I have finally gained some intuition about what the dot-product operation, determinant and eigen-vectors actually represent. Thank you so much.

By Jafed E G

Jul 6, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By Mark A C

Nov 22, 2020

This course has provided me a better understanding of linear algebra concepts specifically on how eigenvalues, eigenvectors, matrices, and vectors can actually be observed or used in engineering (or even in day to day) applications.

By Vijayakumar

May 20, 2020

It was a very good learning and I enjoyed a lot. Hoping to take the advanced level courses in Machine learning and related areas. Thank you very much Professors David dye, Samuel J Cooper and A Freddie Page. Hoping to see you again.

By laszlo

Apr 21, 2018

Awesome course!!! The course is very helpful for those who are willing to build an intuition of linear algebra. The coding assignments are a bit easy for CS students, but allow you to understand what has been taught in the course.

By Laura-Jane D

May 13, 2021

An excellent introduction to the core linear algebra concepts needed to understand ML. I especially enjoyed the emphasis on how matrices transform vectors. It provided me a much stronger intuition of the geometry than I had before.

By David S

Jun 24, 2019

Excellent. Exactly what I needed. A linear algebra course in machine learning. Top notch presentations, materials, and explanations. A nice blend of concepts and detailed calculations especially in transformations and eigenvectors.

By Shwetha T R

Sep 14, 2020

I loved this course! Both Prof David Rye and Prof Sam Cooper were amazing and used brilliant techniques to ensure creative learning. I enjoyed the eigen vectors and values and pagerank algo module a little too much! Thanks a lot!

By PATHIRAJA M P H S

Jul 12, 2020

The course contains very creative introductions to some of the linear algebra theories that I was already familiar with. Could get new intuitions and better, deeper understanding of those concepts. Really glad I took this course.

By Mohamed S

Jun 26, 2020

I liked the course and huge number of exercises. Maybe my only problem is the academic form of the lectures that makes me lost sometimes and forces me to google for an Indian guy who can teach me the concept in a more easier way.

By Rahul S

Oct 28, 2019

This course is little challenging if one has not revised Linear Algebra before, but quite interesting and fun given the examples and utility only after learning the basics of linear algebra elsewhere and then attempting this one.

By Liam M

Apr 4, 2018

This is an excellent refresher of vectors and linear algebra, and although I did it years ago in college I still found some new insights from doing this course. Its all explained very well without being bogged down in formailty.

By Rohan A

Jun 9, 2020

Great course guys! I have done a course on Linear Algebra in my university and watched the 3Blue1Brown series on Essence on Linear Algebra. This course was a good recap of the concepts and their applications in machine learning

By Ramy S R

Sep 27, 2020

Excellent course. Material is explained thoroughly through concise short videos with plenty of visualizations that make linear algebra intuitive. Assignments are chosen carefully and the curated python labs are very enjoyable.

By Prateek K S

May 28, 2018

Nice course. This course is very good to build your fundamental knowledge for machine learning. This course gave me very clean and straight forward understand how mathematics play very important role in machine learning field.

By Himanshu G

Jun 13, 2020

Thank you for designing such a wonderful course. I find difficulty in understanding the concepts related to eigenvalue and eigenvectors and Page Rank. Otherwise, the other concepts have been beautifully explained. Thank you!

By Neelam J U

Jul 14, 2020

I really enjoyed the application of the abstract mathematical concept to real-world problems. This shift from conventional teaching of the subject makes one realise why math is at the core of all technological developments.

By Liu Z

May 6, 2019

As for Chinese students, this course clearly explain the vectors, vector multiplication in a graph way, which for me is very useful, instead of in many Chinese university, which just state formula of calculating the vector.

By Jurij N

Jun 18, 2018

I was very satisfied with the course. I'm really grateful for the effort they put into the programming exercises, so I finally began to put the theoretical knowledge into code. From now on I am able to experiment by myself.

By Fabricio O

May 22, 2019

Great pace and content very nicely curated. Loved it and will carry on with the specialisation. I am a professor myself and I am also learning a lot about good practices when it comes to teaching. Could not recommend more!

By Aldrich W

Aug 17, 2020

Love this course! Prof. David Dye is exceptionally great at explaining these concepts and the British accent also promotes my learning substantially. I'll definitely take the second course in the specialization very soon.