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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

HE

Aug 8, 2021

the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering

CS

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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551 - Mathematics for Machine Learning: Linear Algebra 的 575 个评论(共 2,468 个)

创建者 Aman K S

Aug 18, 2018

This course is a must for all the people who wants to go deep into machine learning and data science as this covers the prerequisites of the courses available.

创建者 Rambabu Y

Jul 28, 2018

It is highly recommended. Very useful to those who are interested in AI and ML and looking for deep dive. Thanks very much coursera and imperial college London

创建者 V A R

Jun 15, 2019

Brilliant course, thought intuitively and had a significant impact on my perspective in viewing transformation and other operations related to linear algebra.

创建者 Zvinodashe M

Jul 26, 2018

Excellent course a little challenging but just the right pace and depth to get one back up to speed with linear algebra, looking to build from this foundation

创建者 Harrison S

Sep 24, 2020

The last assignment (Page ranker) was really complicated. The jyupter platform also showed some issues. But the classes were really really good. I enjoyed it

创建者 DEBJIT P

Jun 21, 2020

This course gives you a solid base on linear algebra. The instructors are very good and the technique that is applied here to teach is unique and attractive.

创建者 Nikhil J

Apr 27, 2020

Great intuition building. Absolutely enjoyed the course. I'm still a little bit behind getting the entire intuition, will come back and get it done. Love it!

创建者 VIJAY N

Mar 31, 2020

Great Course to provide a better perspective to mathematics needed for machine learning. Really enjoyed the course and looking forward for more such courses.

创建者 Kyle W

Dec 5, 2018

Excellent course. It's very practical - focuses on building your intuition of core concepts and applying those concepts through simple programming exercises.

创建者 Sonia .

Sep 10, 2022

Amazing course for building intuition for Linear Algebra. I also liked the fact that only included the parts of Linear Algebra relevant to Machine Learning

创建者 Gurunath G

Jul 8, 2020

Good course. Clears a lot of basics. It gave a different perspective of Matrices and transformations which I did not have earlier. Overall very good course.

创建者 Anastasios P

Dec 22, 2019

Great course to get introductory knowledge and good foundation on linear algebra, especially Eigenvalues and Eigenvectors and some basic python programming.

创建者 Flora L

Jul 29, 2019

This course is amazing. Week 5 quiz is tricky but all the others are fine. The course might take longer than expected to complete but it's totally worth it.

创建者 Joshua M O

Jul 7, 2023

An excellent introduction to Linear Algebra. Clear, concise videos; the assignments are well designed; the quizzes help re-enforce the material. Well done.

创建者 Sujeet B

Jun 19, 2019

Very good; contents covered gives an intuition of what's happening beneath the Mathematics. The lectures are interactive (which keeps your brain working).

创建者 Biplab B

May 27, 2020

A very informative course that is focused on understanding the concepts of linear algebra and its applications rather than on solving numerical problems.

创建者 Adelis N

May 11, 2020

The way this course is structured gives me confidence in myself and allows me to have a better exposure time towards the content. Fantastic way to learn!

创建者 Jia G

Nov 5, 2019

The programming assignments are very well-designed. They are easily to follow and give me confidence to use Python deal with complex mathematic problems.

创建者 Serge K

Nov 25, 2018

I love the stuff that I learned: the usefulness of eigenvalues and eigenvectors, coding pagerank algorithm, gram Schmidt to create orthonormal basis, ...

创建者 Lee F

Sep 7, 2018

Enjoyed the course a lot! It stretched me at times, and I definitely got what I needed and know where to go to fill in any knowledge gaps in the future.

创建者 Zeming L

Oct 22, 2024

The introduction in week 1 about what the vector is and the purpose of machine learning using a contour map is very impressive and simple to understand!

创建者 Khaled A

Oct 3, 2020

If you are new to Machine Learning and mediocre in Mathematics (even though you studied this topic before), then this course is the right place for you!

创建者 Tamanna U

Aug 11, 2020

Over the years, I have learned linear algebra from several sources but this by far enagaged me the most and taught me the intuition more than any other.

创建者 Sumit N

Aug 1, 2020

Wonderful course, highly recommended for everyone who is trying to learn AI/ML/Data Science. Specially for someone who want to brush up their knowledge.