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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
12,157 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

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.

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.

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1076 - 1100 of 2,412 Reviews for Mathematics for Machine Learning: Linear Algebra

By Wambui K

Feb 21, 2019

The instructors were great and the length of the videos is bearable.

By Keith B E

Mar 21, 2020

This course was a lot more useful than the course I got in college.

By Jacint J

Feb 12, 2020

Excellent introduction to mathematical basics for Machine Learning.

By GAUTAM V

Oct 15, 2019

The course materials are communicated very efficiently and clearly.

By Narayan B

Jun 21, 2019

Very good and useful course, worth spending money and valuable time

By Pubudu H

Oct 19, 2023

This Course changed my thinking pattern on vector space completely

By Adarsh K J D

Jul 14, 2021

Great instructors. Explained all concepts in a very simple manner.

By Goh K L

Jun 6, 2021

Impressed by how the instructors can write in reverse so naturally

By Boris B

Apr 18, 2020

Really useful and everybody with passion and motivation can do it.

By Pierre C D M

Mar 24, 2020

Very good very clear. Begineers will definitively have a take out.

By Shilpa S C

Feb 29, 2020

The assignments were hard ,but learned good amount of information.

By Julio V E

May 21, 2018

Excellent course, with a great team of well-motivated instructors.

By Alessio T

Sep 18, 2022

Very clear explanations of very important and interesting topics

By Berlian P R H

Mar 12, 2021

this course is improve me basic of mathematics. thank you so much

By George P

Sep 10, 2020

Demanding but worth it. You have got to mirror the bear to see...

By MICHAEL P A P

Aug 3, 2020

Si colocan subtitulo en español seria fantástico.

Excelente curso.

By Mukunda M N

Jan 25, 2020

Intuitive explanations rather than jargons, very enjoyable course

By manoranjan s

Nov 5, 2019

I really enjoyed this course. This was a good refresher for my LA

By Alexander v d P

May 22, 2018

Very clear quick browse through some concepts from linear algebra

By YW

Apr 11, 2018

excellent course for freshen-up linear algebra, highly recommend!

By Daniel M

Feb 14, 2021

Fundamental to understand the foundations of the Linear Algebra.

By Akshay K

Feb 12, 2020

Best course to start off with for people stepping into ML field.

By Ravindu T

Oct 3, 2019

The course is very informative and really interesting to listen.

By Avi M

Aug 14, 2019

Good for developing linear algebra intuition and helps visualize

By Xiaofei Q

Sep 9, 2024

Very helpful for improving my understanding of machine learning