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

4.7

stars

4,603개의 평가

•

833개의 리뷰

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....

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.

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.

필터링 기준:

교육 기관: Wookjae M

•May 03, 2019

It was a "neat" lecture for understanding the basic of linear algebra. Programming assignments and test were well designed. Thank you for the lecturers.

교육 기관: Gabriela S

•Nov 03, 2019

Great approach, teaching the intuition of mathematics, this is exactly what I was looking for! Thank you to the amazing instructors for the fun course!

교육 기관: Rushil

•Sep 03, 2018

Fantastic recap on linear algebra concepts.

The focus on intuitive understanding is a pleasure and far more engaging than more traditional approaches.

교육 기관: Aman A

•Oct 09, 2019

One of the most concise and yet complete courses on Linear algebra in the light of its practical application in the real world and machine learning

교육 기관: Mohammad A M

•Oct 22, 2019

This course gives you an in-depth understanding of Linear Algebra concepts that are momentous for Machine Learning, so DO NOT hesitate to take it.

교육 기관: Hritik K S

•Dec 08, 2018

I learned the best visualisation of linear algebra's concepts. Nothing is better that understanding the concepts and how the things are happening.

교육 기관: Amod

•Jun 11, 2018

Extremely Helpful.Every Machine Learning Aspirant should complete this course to get the basics right! Instructors and Course Content are perfect.

교육 기관: yifei l

•Dec 21, 2019

Great linear algebra part, compare to regular linear algebra class. This class focues more on intuitive and practice. I really enjoy this class.

교육 기관: ChaoLin

•Oct 25, 2018

only the homework is not so friendly to the people who do not use python often, and the other is so good, especially about the teachers, thanks!

교육 기관: Nigel H

•Apr 18, 2018

Very high production standards, well presented by enthusiastic staff and very manageable as the material is taught so well. Highly recommended.

교육 기관: Brandi R

•Jun 20, 2019

Wow, this course was hard. But very good, I learned so much about transforming vectors and matrices as well as some interesting Python coding.

교육 기관: Oj S

•Dec 13, 2019

A very useful course for Machine learning, you will never feel aloof if you are not with a Mathematics background while learning ML afterward.

교육 기관: Asrorbek O

•Jun 21, 2019

The course is very great. I have thoroughly enjoyed taking it. However, 5th module should be improved by teaching diagonalization more deeply.

교육 기관: Robert P C J

•Jul 01, 2018

This course was a really excellent refresher in linear algebra! Everything was presented clearly, and the lectures and homework were engaging.

교육 기관: Brian A W

•Dec 12, 2019

Very informative, even as I start my postgraduate studies, I have picked up on a few notions and perspectives I have never considered before.

교육 기관: Nano D N

•May 14, 2019

I'm only at the beginning of the course but the material is really worth it.

Great instructor. He makes himself very clear and easy to follow.

교육 기관: Camilo J

•Feb 18, 2019

Great class and wonderfull material. Focused on intuition and programming rather that minndlessly solving problems as a mechanical challenge.

교육 기관: Yuen P H H

•Apr 02, 2018

Website works really well and the course material is clear and concise where the lecturer describes the theory and its application very well.

교육 기관: Julio A S H

•Nov 03, 2019

This course is very well designed, I loved it!

I would like to see an Imperial College London design a fully fledged Machine Learning Course.

교육 기관: Yevhenii S

•Feb 15, 2019

Very good course. It well structured, good lectures and assignments. It gives enough intuition and refresh to move forward. Thank you team!

교육 기관: Dilver J H G

•Nov 06, 2019

I strongly recommend to follow this course, it's challenging at times but it's worth it in order to build a solid base in machine learning

교육 기관: Srimat M

•Aug 18, 2019

awesome learning experience, great visualization of number and its just number which can mean a lot!!! must take course... thank you team!

교육 기관: Pratyush

•Jan 21, 2019

The course is a great one for someone who has been through high school mathematics. On hand questions are practiced using machine learning

교육 기관: 郭宇

•Jan 09, 2019

Einstein Notation is very useful, and I hadn't heard of it before the class. With this tool, I can easily derive formulas in MATRIX form.

교육 기관: Sungbae C

•Sep 28, 2019

Extremely useful, but I would not recommend to take it without ANY prior knowledge of linear algebra, as the course's pace is quite fast.

Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.