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返回到 Mathematics for Machine Learning: Linear Algebra

学生对 Imperial College London 提供的 Mathematics for Machine Learning: Linear Algebra 的评价和反馈

4.7
12,504 个评分

课程概述

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....

热门审阅

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.

GB

Aug 16, 2020

The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.

筛选依据:

1101 - Mathematics for Machine Learning: Linear Algebra 的 1125 个评论(共 2,469 个)

创建者 Adarsh K J D

Jul 14, 2021

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

创建者 K L G

Jun 6, 2021

Impressed by how the instructors can write in reverse so naturally

创建者 Boris B

Apr 18, 2020

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

创建者 Pierre C D M

Mar 24, 2020

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

创建者 Shilpa S

Feb 29, 2020

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

创建者 Julio V E

May 21, 2018

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

创建者 Sean L

Oct 3, 2025

gave me a very clear intuition about what's under the computation

创建者 Segundo F D T

Jan 1, 2025

Excellent course, with good examples and very clear explanations.

创建者 Alessio T

Sep 18, 2022

Very clear explanations of very important and interesting topics

创建者 Berlian P R H

Mar 11, 2021

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

创建者 George P

Sep 10, 2020

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

创建者 Michael P A

Aug 2, 2020

Si colocan subtitulo en español seria fantástico.

Excelente curso.

创建者 Mukunda M N

Jan 25, 2020

Intuitive explanations rather than jargons, very enjoyable course

创建者 manoranjan s

Nov 4, 2019

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

创建者 Lex v d P

May 21, 2018

Very clear quick browse through some concepts from linear algebra

创建者 YW

Apr 10, 2018

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

创建者 Daniel M

Feb 14, 2021

Fundamental to understand the foundations of the Linear Algebra.

创建者 Akshay K

Feb 11, 2020

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

创建者 Ravindu T

Oct 3, 2019

The course is very informative and really interesting to listen.

创建者 Avi M

Aug 14, 2019

Good for developing linear algebra intuition and helps visualize

创建者 Alexey Q

Sep 9, 2024

Very helpful for improving my understanding of machine learning

创建者 Eduardo Z R

Jan 29, 2022

Nice and complete course with the essential knowledge to apply.

创建者 Alexandre G

Jun 3, 2021

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

创建者 premjeet p

Aug 10, 2020

Excellent amount of knowledge presented in a simpler visual way

创建者 李赞

Apr 3, 2020

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