Back to Mathematics for Machine Learning: Linear Algebra

4.7

stars

8,064 ratings

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1,622 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 10, 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 26, 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 Mia C

•Aug 02, 2020

I have just completed the first of the 3 courses in this series. First of all, I must thank Professor Dave for the excellent efforts he put into delivering those first 4-week of fun and excellent topics. You have made it so interesting to me that I would love to know more about linear algebra. (*** Not only that, you have taken us on a informative journey of shopping for apples, bananas, and carrots to some visits to bears! : ) : ) ***)

For week 5, Professor Sam had given me some excellent materials as well. I look forward to taking the second course in the very near future.

Both professors are so smart and clearly great teachers! I feel so lucky to have stumbled into this course!

THANK YOU BOTH!!

By Warul K S

•Jun 28, 2020

The representation of mathematical concepts as "tools" to solve practical problems was beautiful and enabling, the way the instructors build our intuition rather than providing us with a bland approach to simply solving mathematics questions was phenomenal, the structure of the course was definitely first class as one would expect from Imperial. We were guided through the assignments but not fed the answers, our understanding was tested and additionally built upon through each exercise. Overall, I would recommend this course to anyone studying the subject in college or desiring to build a solid mathematical foundation for machine learning or even simply to appreciate the beauty of mathematics.

By VARUN S

•Sep 15, 2020

The reason I liked the course is its focus on important topics. Many complain that it was not for beginners and I understand the frustration. They shift gear in some assignments, but you have to put time to explore and self-study the material to move ahead. That is how the real learning happens. I think there was an advantage of not having a specific reference book. I have tried other LA courses and these books can absorb all your time and you may find yourself at the same place after 6 months. On contrary, this course forces you to go out and study only the topics you are struggling with. All in all, one of the best MOOCs I ever attempted ... Cheers!

By Anikesh M

•May 16, 2020

The course is extremely interesting and fun to do. Instructors have put a lot of efforts to make some complex topics seem easy and engaging. I could relate the calculations being implemented into practical ML applications. But i would also like to add that the last module of eigen-values and eigen-vectors gets very confusing especially the page rank algorithm and the quiz of eigen values and vectors..If the instructors could add a video or two to explain some more concepts, the course would become a perfect package even for a beginner.

AT LAST I WOULD THANK IMPERIAL COLLEGE LONDON FOR MAKING A FABULOUS SERIES. I REALLY LOVED LEARNING FROM YOU.

By Iacopo C

•Aug 22, 2020

Its purpose is to build the intuition behind the fundamental, important topics of Linear Algebra required in Machine Learning, it aims to develop the insight required to access other courses on Machine Learning Tools and as such it does an amazing job.

The clarity and enthusiasm of both the instructors is priceless and when a subject like this is communicated so effectively it makes a huge difference.

If what you're looking for is an understanding of the concepts that are the foundation of ML together with some rigorous exercises that will help you solidify you knowledge then I definitely advice on enrolling into this course.

By Soumya A

•Jul 22, 2020

The course is great especially for a brush up of first year concepts for someone like me, but I couldn't help smiling throughout after realizing that the way this entire course series is presented is by reflecting the video lectures while they write on a transparent board about a vertical mirror plane. Although this must have been rather simple to implement, the idea is something incredible to my mind. Classic college education never exposed me to the ideas and a need to 'build intuition' for things like this course has. Kudos, professors!

By Hasala S k G K

•Sep 07, 2020

Excellent course! I serve in academia with a terminal degree in mathematics, but yet I learned many interesting connections specifically aligned with applications. This course is designed in a way it can be easily followed by anyone with a basic mathematical background but, yet can equally be enjoyed by someone with a strong mathematical background. After taking this course, I got motivated to enroll in 'Multivariable calculus' and PCA to complete the specialization. Thank you all who put the course (and the specialization) together!!!

By Aditya N P

•Apr 27, 2020

I found this course excellent. For quite a long time, I have been struggling to understand what Eigenvectors and values mean and why do we bother to focus so much on Orthonormality. This course dealt with these concepts in a simple and lucid manner. It built the necessary math and intuition, which I liked the most. Also, this course really explained well why Matrices and its knowledge is important as it is useful in so many applciations. I am happy with the course and expect the same utility from the next course in the specialization

By Mikhail D

•May 08, 2020

I see why some people are unhappy with this course (may be difficult to follow if you haven't seen Linear Algebra before / assignments are sometimes confusing), but I personally loved it. I took this course as a refresher for the main Linear Algebra concepts I last studied almost 10 years ago and the lecturers did a great job presenting the material in a very visual, intuitive, and over high-level way, making sure that you really get a feel of the main concepts instead of being buried in notation and formulas.

By jie

•Jun 05, 2020

This course is a great introduction course to linear algebra. I am a quantitative finance guy and, to be honest, already forgot almost 90% of linear algebra I learned in college. It is always a little painful for me to code matrix multiplication during my work. I found this course before I went to amazon to buy a text book. This course saved me so much time and I really learned what I need to learn. Thank you so much.

The only con of this course is that: the python coding assignment is too easy.

By Ameya P

•Jun 25, 2020

Course would be easy if you have any background with linear algebra, but concepts through the scope of geometric application, which is fresh give new perspective to whole linear algebra than how its taught in class or Uni. Amazing instructors ! and great way to visualize things ! Please note This course is not suited for beginners and people looking for an introductory lecture to Linear Algebra! please have some introductory knowledge before you start off with this course . Thanks !

By Emil Y

•Jun 17, 2020

Excellent course to get you to refresh or provide you with solid foundations of linear algebra, provided you supplement the course content with additional reading where you are either a bit "rusty" or completely new to a particular topic. I also find the quizzes and assignments particularly helpful in cementing your understanding of the material. Overall, I had a great experience and will strongly recommend this course to anyone on the quest to become a data scientist.

By Xin Y

•Apr 09, 2020

This is an excellent course as a refresher of the basic concepts in my college linear algebra. The instructors really put a lot of effort into making all the course materials. I enjoy the animations a lot! I am not a pro in Pandas but the programming assignments are actually very well-explained and perhaps a bit too easy. I'd thought they would put some plots and twists in the programming assignments. Very helpful course and great instructors. Thank you!

By Maximiliano B

•May 24, 2020

This course is excellent and it provided me a very good refresh about the linear algebra theory that I’ve learned in my graduate studies. The professor are great, the videos have an appropriate duration, and they help you build the intuition incrementally every week. The Python assignments are relative easy but they are of great value. I definitely recommend this course and I am looking forward to start the next course of the specialization.

By Orlando F

•May 24, 2020

A comprehensive course in Mathematics and Linear Algebra. If you're not related, or with rusted maths, don't be afraid, it will work for you, but it will demand some amount of time. A good time of course. Here I learned things I didn't fully understand. Great teachers. Some misses on explanations, will push you to Khan, tutorials, or books. Recommended course for everyone interested in getting in ML, AI, DS. A great introductory course.

By Natasha M D

•Aug 27, 2020

Excellent course for those who like me struggle with intuition of math behind machine learning. This is not for beginners and it is not a general linear algebra course, it assumes that you have already a good grasp of the theory. The course for me took the theory I had and increased my level of understanding in how to apply it to machine learning. Also the videos are fantastic, I've never been so enthusiastic about doing math before :D

By Prateek A

•Jun 22, 2020

Very very excellent course on Linear Algebra by Imperial College of London :

I would like to thank @David Dye for teaching the intuition and essence of Linear Algebra.

Also @Sam Cooper, what a great teacher he is, couldn't wait to start the next course of the Specialization.

The best thing about this course is that whatever we learned, we applied all the stuffs side by side in ML.

Absolutely enjoyed the course. Thank You Coursera

By Harsh D

•May 03, 2020

Certainly the best online courseware I have attended. Prof. Dye breaks down most typical concepts of mathematics in simple and easy to understand blocks that makes this course fit for anyone. He brings out an interesting dimension to every concept that makes you comprehend it well and you're equipped to understand the practical applications of it. Would recommend to anyone looking brush their concepts of linear algebra.

By ChristopherKing

•Mar 22, 2018

This is such a great course for student already have background about college level linear algebra knowledge, but don't know the under relationship among those terminologies. For instance, after this course I finally know what is dot product means, what is eigen characteristics. The content of this course are well prepared, this is such a masterpiece from Imperial College London. Thanks to all stuff behind this course.

By Srutimala D

•May 12, 2020

The connection between machine learning ad vectors got clearer as the course moved ahead. The quizes are detailed and requires actual understanding of the concept which is not hard to grasp once you pay attention to the lecturers who themselves are so passionate about the subject, makes me excited to learn too. I can say, I finally, after leaving high school, have understood high school maths and it's applications.

By Ashish D S

•Apr 09, 2018

This is excellent course on Linear Algebra. The best part of this course is, lectures focus on the physical interpretation of the topics rather than making you practice formulae without understanding. This course helped me refresh my Linear Algebra concepts and also helped me better understand change of basis and Eigen related concepts.

Many many thanks to professors for excellent course design and presentation.

By Ritesh S

•Jun 28, 2020

No one can hate mathematics. The only reason you hate it or don't visualize it is because you never had an instructor who could do it. But, this course solves this problem with beautiful designed course content and intuitive quizzes that help you understand the underlying concepts on a broader perspective. Want to understand and visualize the basics of Linear Algebra used in ML, this is the course to apply to.

By Rajakrishnan S

•May 29, 2020

awesome content with excellent pace. no bullshit during lectures. only place for improvement would be to give relevant content in readings as the course feels just of videos and less reading materials for reference. Ofcourse ,one can look up in textbooks , but giving the reading materials in the course will improve the readability and findability and will be according to the lecture content. thanks for asking!

By Vincent L

•Jun 09, 2018

I took this course as a review for my data science curriculum. Previously, I was having trouble recalling the details of matrix arithmetic which was making it hard for me to get a deeper understanding of machine learning. After doing this course, you should have no trouble following along. For those already familiar with the material, it should take about 1-2 weeks to complete if working at a leisurely pace.

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