Back to Mathematics for Machine Learning: Linear Algebra

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10,222 ratings

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2,054 reviews

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....

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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By Aleena T

•Sep 13, 2020

Excellent course for anyone who wants to know the nuances of Linear Algebra and its applications.The applications are not just mentioned,but one gets hands-on experience applying the concepts they learned,in code.Hats Off to the entire team!

By Shivam K

•May 26, 2020

The course is really helpful to those seeking clarity on the concepts. Week 4 and 5 really will really demand your attention. Loved every single bit of this course.

Would be glad if course would have included more visualisation to play with.

By CHIOU Y C

•Jan 2, 2020

This is a good linear algebra course intro. May not be the one for who is looking for mathematical rigorous but it's enough for machine learning. Linear Algebra is important but not all topics and this course highlights the needed materials.

By Jaromir S

•Sep 30, 2019

I needed a quick refresh of my prior knowledge of linear algebra for my MSc course and I wasnt disappointed. I also appreciated the complementary python exercises and the effort to put the material into a context of a real world application.

By Someindra K S

•Jan 3, 2019

I got a lot of intuition about some fundamental aspects of linear algebra. Rest of courses on maths was very rigorous in terms of methods. This was more inclined towards applications in machine learning. I enjoyed the entire learning process

By Stefan B

•Apr 8, 2018

It was fun to work through the course. Sometimes it was challenging as it has to be. Now I have a much better understanding of the topic. Especially appreciated is the approach of the instructors to build intuition: it worked for me, thanks!

By Souvik G

•Feb 8, 2021

I have never visioned mathematics the way it was taught here. I believe every Engineer may he/she be a an ML engineer or not must take this course to just fall in love of mathematics. This course will inherently motivate you to dig deeper.

By Danilo d C P

•Jul 19, 2019

I really enjoyed taking this course. I could review and learn for the first time some important topics for machine learning, in special the eigenvalues and eigenvectors classes. I'd like to thank the course's professors and collaborators.

By Omar R G

•Mar 17, 2019

An excellent course on the fundamentals of linear algebra. It was great revisiting all this topics. I would also say that some knowledge on linear algebra would be useful for taking this course given the fact that the lectures are quick.

By Felipe C

•Nov 29, 2020

Very good course. I liked it a lot. Some abstract thinking required. The last week is a bit less well explained but OK nonetheless.

In my experience, the estimated times for completing the work are a bit optimistic, it took me more time.

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.

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