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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
11,954 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.

C

Mar 31, 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

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1051 - 1075 of 2,367 Reviews for Mathematics for Machine Learning: Linear Algebra

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 Eduardo Z R

Jan 29, 2022

Nice and complete course with the essential knowledge to apply.

By Alexandre G

Jun 3, 2021

A clear and informative course that re-cindle my love for maths

By premjeetprasad

Aug 10, 2020

Excellent amount of knowledge presented in a simpler visual way

By 李赞

Apr 3, 2020

it's quiet short but contains most of the important imformation

By Wenwen L

Aug 20, 2019

Hope this module can covers more about dimension reduction etc.

By Hao G

Jan 27, 2021

Very good course materials, but the marking system is annoying

By Ashok M

Aug 12, 2020

Must watched Course for Machine learning and all Mathematician

By Vikas K P

Jun 29, 2020

good course to understand the mathematics for machine learning

By Danielius K

May 23, 2018

An amazing introduction to a crucial part of machine learning!

By AP P

Jul 24, 2023

Very good for beginners interested in artificial intelligence

By Bob K

May 17, 2021

Provides excellent intuitions + how to compute. Recommended!