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Mathematics for Machine Learning: Linear Algebra(으)로 돌아가기

임페리얼 칼리지 런던의 Mathematics for Machine Learning: Linear Algebra 학습자 리뷰 및 피드백

4.7
별점
7,543개의 평가
1,506개의 리뷰

강좌 소개

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....

최상위 리뷰

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.

CS

Apr 01, 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.

필터링 기준:

Mathematics for Machine Learning: Linear Algebra의 1,504개 리뷰 중 1326~1350

교육 기관: Xinsong D

Jun 15, 2018

Excellent, but for the pagerank part, the instructor teaches a little bit fast.

교육 기관: Rocber

Sep 30, 2018

it is really useful to help me build geometric meaning with vector and matrix.

교육 기관: Venkata P S

Jun 01, 2020

The course will help us to apply the matrices for machine learning algorithm.

교육 기관: Tarun A

May 21, 2020

after learn from this course, i know very well about machine learning

thanks.

교육 기관: Aviral A

Dec 10, 2018

A good course for gaining knowledge for Linear Algebra for machine learning.

교육 기관: 胡与诚

Apr 02, 2018

Good course, But I think it should explain more about the underlying things.

교육 기관: Surya S

Jul 13, 2020

Was a but fast. For non engineering or people who are new it will be tough

교육 기관: Abhirup B

Jul 29, 2020

goood course well designed qizes and aasignments time saving yet fruitful

교육 기관: Julian A

Jun 12, 2020

Fantastic course that provides a great introduction into linear algebra.

교육 기관: Ivan

Jun 04, 2018

The course content is good, but the programming assignment is too easy.

교육 기관: Ritik j

Jun 01, 2020

some topics are explained in a typical way and a bit problem was occur

교육 기관: KIRANKUMAR M

Jul 21, 2020

Its is the best course to know about matrices and their applications

교육 기관: Sharad K L

Mar 09, 2020

Exams were hard and most of the exams were source of the knowledge.

교육 기관: JOSÉ M B D

Jan 25, 2020

excelente curso, me gustaría que se complementara con programación.

교육 기관: Ng Y Y

Jun 21, 2019

Good overview and introduction to key concepts of linear algebra

교육 기관: Fatima,Safa

Jun 18, 2020

Wish it was a bit more spontaneous but overall great content!!

교육 기관: Gautam K

Mar 07, 2019

Highly recommended course for beginners in Machine Learning.

교육 기관: Mark R

Jan 03, 2019

Good grounding in the fundamental mathematics needed for ML

교육 기관: Alagu P P G

Jun 18, 2020

good start up for algebra enthusiasist.

a strong foundation

교육 기관: Gana

Apr 23, 2020

I felt that lectures aren't enough to solve the exercises.

교육 기관: celwang

Mar 24, 2020

good course ! but some of the formula should be more clear

교육 기관: wanglanri

Jun 25, 2018

The core idea of eigenvalue and eigenvector is very good.

교육 기관: Alisa G

Apr 25, 2020

great teachers, very practical quizzes and examples!

교육 기관: Long Q

Oct 10, 2018

not bad, I feel the information is not enough for ML

교육 기관: Rahul S

Jun 21, 2020

like a building blocks for one step forward into AI