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

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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....

Dec 23, 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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: Xiangxin S

•May 29, 2018

great class, only focused on the core concepts of linear algebra, and try to build a intuition of how these concepts fit in later on machine learning applications

교육 기관: Ali A

•Jun 08, 2018

One of the best I've seen on linear algebra. I would recommend it for people who need a refresh on the subject.

교육 기관: Prateek J

•Apr 30, 2018

Good course for refreshing your knowledge in Linear Algebra. Easy to understand material and instructor is great.

교육 기관: Kohinoor G

•Apr 24, 2018

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: Rhian T

•Jun 18, 2018

Clearly presented and engaging through the course's focus on Data Science problems.

교육 기관: J. W

•May 10, 2018

I took Linear Algebra in undergrad nearly 20 years ago. The instructors for this course developed the inuition behind core concepts in such a way that it made the material very accessible and provided a great basis for further study using supplementary material. I am pleased with the overall presentation.

교육 기관: Deepak K A

•Jun 19, 2018

:)

교육 기관: Stephen B

•Mar 28, 2018

Very well put together, well presented and easy to understand. One of the best MOOC's I've ever taken.

교육 기관: Ezequiel A

•Jul 09, 2018

Amazing course! Thank you so much

교육 기관: nicole s

•Apr 17, 2018

Great teacher and teaching style!

교육 기관: Arihant J

•Jul 02, 2018

Simply excellent and highly recommended !

교육 기관: Satyajit S

•Mar 18, 2018

Great introductory course. Linear Algebra is quite often the most poorly taught/understood subject in college mathematics.This course has a done a great job in stressing on the core concepts without focusing on the computational details which happens in typical linear algebra courses

교육 기관: Michelle W

•Jul 03, 2018

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: Iurii S

•Mar 28, 2018

Great intro into vector algebra - helps build better intuition even if you know all the formulas already.

교육 기관: amardeep

•Apr 18, 2018

best math course for ML

교육 기관: Felipe M

•May 02, 2018

Great Teacher!

교육 기관: Ahmed R

•Apr 22, 2018

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: George C

•Jul 07, 2018

Excellent course -- very good lectures and a useful introduction to the topic

교육 기관: Arun B K

•Apr 12, 2018

If you are a bottom-up person who has to understand nuts and bolts before going up the software stack, this is the course to start your machine learning study. - From a ML newbie

교육 기관: Takayuki M

•Mar 24, 2018

(๑•̀ㅂ•́)و✧

교육 기관: Marvin P

•Mar 17, 2018

I'm impressed how good this course is. It provides lots of intuition, many examples and exercises about the topics and Prof. Dye is a incredibly talented instructor. Thanks a lot!

교육 기관: Kuo P

•Mar 16, 2018

excellent

교육 기관: Jayant V

•Mar 29, 2018

I have taken a course on linear algebra during my graduate program and must admit that it was not one of my more comfortable ones! Coming back to this course online, it really did help me get a much better understanding of concepts like dimensionality, basis, eigen values and eigen vectors. I intend to go over the lectures at least a few more times to be sure I have understood it well.

교육 기관: Jack C

•Apr 06, 2018

Great course, well presented videos and challenging but engaging content. Great high level view of linear algebra to give you a starting point for other courses. May be useful to have some machine learning knowledge before taking - Andrew Ng's course would serve as a good counterpoint.

교육 기관: Aditya K

•May 21, 2018

Fantastic course that develops the intuition behind Linear Algebra. I'm glad I did it.

I would couple this course with MIT prof Gilbert Strang's lectures on linear algebra on MIT OCW.