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

별점

10,700개의 평가

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2,126개의 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....

EC

2019년 9월 9일

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.

PL

2018년 8월 25일

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.

필터링 기준:

교육 기관: Callum M

•2021년 6월 9일

I thoroughly enjoyed this and made studying Machine Learning at the side of this a lot less of a guessing game when it came to the maths. The quizzes and programming assignments are tough but once you figure them out you feel confident with your understanding going forward, so I guess they do the job! Both lecturers are brilliant too - really clean presentations.

교육 기관: RHEA R B

•2020년 5월 20일

This course was very informative . Having learnt to solve most of this problems by hand in under-graduation , this course helped me to code these hand-worked problems . Additionally I was able to understand and visualize what the problems actually do . I highly recommend this course for anyone who is looking to learn or advance their career in machine learning .

교육 기관: Art P

•2018년 6월 8일

This course was of high quality, was very helpful in explaining some key concepts and I appreciated the instructors energy and humor. My only complaint about the course is that some of the quizzes and homework assignments felt significantly more challenging than what was covered in the lessons; however, the discussion forums proved helpful in closing this gap.

교육 기관: Alina I H

•2020년 11월 18일

Really good overview and while explained perfectly by the instructors (using different media that would have been amazing to have back in school...) still challenging enough to get the brain cells running. Fun to do, yet one should take time and really concentrate. Thanks for this amazing opportunity! I'm sure this knowledge will really help me along the way.

교육 기관: Sridhanajayan S

•2020년 5월 31일

This is an exceptional course for learning Linear Algebra in an intuitive way. i would recommend this course to everyone who is fond of mathematics. This course will also have programming assignments with python and numpy packages. Overall I had a wonderful experience and a handful of knowledge. Thank you for the course creators and professors and lecturers.

교육 기관: Ollie D

•2020년 7월 9일

For someone having already graduated with a degree in Mathematics, the mathematical concepts centred around this course were easy to understand, but then applying this knowledge in to code was challenging. Which I was expecting it to be given my lack of experience with python and jupyter notes. A worthwhile course for anyone looking in to data science.

교육 기관: David P

•2018년 7월 10일

Great content, lecture videos are brilliant. I would make one suggestion; it would be great to have more examples or even recommend text books that we as learners can download or purchase, this will assist those who wants to learn these techniques in practical examples. Other than that I have learned alot and will continue using coursera, good job guys

교육 기관: Ahmed R

•2018년 4월 22일

This is a very good introduction and review of Linear Algebra. The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics. I also learned where I need to learn more in order. Overall will recommend either as a review or a stepping stone to learning more about Linear Algebra.

교육 기관: Kohinoor G

•2018년 4월 24일

One of the best Linear Algebra [LA] courses for beginners/novices. It takes away the drudgery of algebra and formulae and tries to explain the "essence" of LA. This is by no means comprehensive LA course - but good enough for people who are fed up with "this is how to calculate the Eigen vector/determinant/<insert pet peeve>" mode of teaching LA.

교육 기관: Kerr F

•2020년 6월 23일

Brilliant course which helped me to re-learn/learn linear algebra methods for machine learning! The course instructor videos, course structure, worked examples and assessments were all extremely useful and allowed me to achieve my learning goals. I would recommend this course to anyone (but would maybe first suggest brushing up on basic python).

교육 기관: Jonathan S Y P

•2020년 4월 11일

Me parece un curso muy bueno, es básico pero la verdad hay que practicar mucho haciendo ejercicios y no conformarse únicamente con la información de los vídeos, si no, buscar otras fuentes para complementar. Para ser básico fue un desafío porque hay problemas que aparecen en los exámenes que requieren de mucho análisis. Vale la pena; me gustó!

교육 기관: Alin A

•2021년 3월 25일

If you are completely new to the subject you may find the course a bit challenging at times but after taking several traditional courses on the subject I can say that by far this is the best course on linear algebra I ever took! Well balanced, straight forward, and great intuitive approach, as well as very neat production! Highly recommended!

교육 기관: Kisan T

•2020년 3월 9일

This course has helped me to understand the basics of linear algebra and it's application in computer science. I was aware of mathematical calculations of the linear algebra, but I did not know reason and meaning of those calculations. I am grateful to Imperial College London and Coursera team for giving me opportunity to take this course.

교육 기관: Mrinal M

•2021년 6월 27일

It is a brilliant course, both the instructors did a great job in making is clear and interesting. This course makes the subject really interesting to learn and gives you really good intuition about operations using linear algebra. The thing I loved about the course is that, it covers only parts of linear algebra that is useful for ML.

교육 기관: Divyaman S R

•2020년 10월 31일

Excellent course with the just right amount of detail to expose beginners to the concepts of linear algebra. I look forward to other courses from ICL in coursera in the filed of mathematics, data science and machine learning.

Thanks to this course, I am in love with linear algebra and am continuing further self-study on this subject.

교육 기관: Duc D

•2019년 9월 22일

Awesome content and very clear lectures. Would be great to have links to more in-depth explanations of certain unexplained assumptions. For instance, it's unclear how the characteristic equation comes about (by assuming that the characteristic matrix does not have an inverse) and also why the page rank matrix is setup the way it is.

교육 기관: 谢仑辰

•2019년 2월 27일

I really appreciate staff of ICL's effort to bring us such an intuitive and straightforward course. It's totally different from those linear algebra courses I've received in China. From your idea on explaining this course on space and transformation, I started to build a strong foundation about linear algebra, and machine learning.

교육 기관: Gabriel W

•2020년 5월 23일

I did the 3 specialization lessons "Mathematics for Machine Learning" (Linear Algebra, Multivariate Calculus, PCA). I really had a lot of fun and learnings in the first one (5 stars for Linear Algebra): David Dye is an increadible teacher. Thank you for your enthousiastic Knowledge Transmission: Mathematics are very cool with you!

교육 기관: Niju M N

•2020년 4월 9일

This course lays the groundwork for the Algebra required in ML. The basics are covered really well.There are quizzes and assignments to strengthen the ideas learnt in the course.At times felt the assignments are very easy .It can be used to brush up the basic Algebra or learn from Zero. The instructor explains every thing clearly

교육 기관: Paul K M

•2019년 10월 9일

This course gives a good overview of linear algebra using python numpy arrays. It doesn't go super deep into the topic, but I wouldn't call it superficial. It requires you to do some basic vector and matrix algebra by hand, build agorithms to do some of those calculations, and introduces some numpy methods for those operations.

교육 기관: Michelle W

•2018년 7월 3일

Excellent course. I have never taken a linear algebra course before, so it took me longer to complete this as I had to learn the basics to follow the material in this course. The course is designed as a review of linear algebra, but if you are motivated and have time, it's possible to complete without having had linear algebra.

교육 기관: Alex H

•2020년 2월 9일

This is exactly what I wanted from an online course! I took linear algebra at university decades ago, but made the mistake of learning just enough to pass the next test. The lectures in this course laid out and solidified concepts for me which were previously abstract. The presenters were clear, concise and, I daresay, fun!

교육 기관: Benjamin E

•2020년 2월 24일

This is a good course that allows you to develop a high and low level understanding of linear algebra...unlike a certain university professor I had who made us do 5x5 matrix transformations by hand. I highly recommend doing outside reading alongside the course to expand your knowledge, especially regarding the coding aspects.

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