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

4.7

3,472개의 평가

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623개의 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....

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.

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.

필터링 기준:

교육 기관: 田德宇

•Jun 23, 2019

no lectures, only videos

교육 기관: JAY C

•Jun 30, 2019

I think the concepts are explained clearly, with ample examples to real life. Much easier to understand this time around. The coding labs could use more pointers though.

교육 기관: Augustinas S

•Jul 03, 2019

Fast paced linear Algebra, perfect to get refreshed. Might be too concise for those who learned Math not in English a few decades ago, will require to browse Forum for additional links to read on the side.

교육 기관: Keyuan W

•Jul 08, 2019

Basic knowledge about machine learning, but very useful, maybe this course should be tagged as higher level, instead of beginner.

교육 기관: Earneet

•Jul 08, 2019

i don't know , just finish it so soon

교육 기관: Cici

•Jul 12, 2019

This is a great course. The only thing is sometimes the calculations are hard to follow. I wonder if it is possible to let viewers click through a calculation process at their own pace. But the instructors are great!

교육 기관: Valentinos P

•Jul 13, 2019

An outstanding course which builds your mathematical intuition rather to prepare you for mathematical calculations. My opinion is that its contribution is significant in the pool of courses in coursera.

교육 기관: Horacio G D

•Jul 19, 2019

Really intuitive course on matrix algebra with very clear geometric explanations.

교육 기관: Rick M

•Jul 21, 2019

Overall, I thought this course was worth the time. Some of the material was challenging, but the instructors were pretty good at explaining clearly. Just a head's up: there is relatively little reading material here, so if you struggle to learn through videos you might have a hard time. That part was a challenge for me.

교육 기관: Sydney F

•Jul 26, 2019

While they explain the basic concepts of linear algebra, sometimes the programming assignments are tricky and some of the quizzes are too complicated to complete with our current knowledge. However, the course is worth taking if you want a solid math background for machine learning.

교육 기관: Berkay E

•Jul 26, 2019

Some of the concepts are unclear. You need to make extra research to understand whole concepts.

교육 기관: João M G

•Jul 29, 2019

The course is a good review of linear algebra for machine learning. But It would have been better if there were more code exercises and if they were more challenging.

교육 기관: David N

•Aug 04, 2019

Strong basic preparation, but I feel that it stops too short. There should be a module 6 and a module 7 covering intermediate-level topics.

교육 기관: Gabriel L S

•Aug 12, 2019

I like the the structure of explaining the theory using examples (in this case, geometric/visual examples). However, I would love to have further understanding on the basic linear algebra topics (or at least be linked to websites that explain this further) to allow flexibility to students like me who has zero knowledge on linear operations. Overall, I was able to overcome the challenged through self learning, understand the concepts well, and appreciate the applications in machine learning.

교육 기관: Weiyu G

•Aug 12, 2019

It is really intuitive and good for people who have little idea of Linear Algebra. The best part is the PageRank Algo.

교육 기관: Gurudu S R

•Aug 18, 2019

1.Need more clarity on calculating Eigen vectors using back substitution of Eigen values.

2. Power Iteration method for the Page Rank Algorithm should be more specific and clear.

교육 기관: Cindy X

•Dec 21, 2018

I think this course is a little bit hard for a beginner with python. And I hope that the teacher can talk more about the Machine learning part.

교육 기관: Manuel M

•Jan 26, 2019

The course feels very disorganized in general. Some quizzes are about 10 standard deviations from the average difficulty, which is befuddling to say the least.

교육 기관: Nathan C

•Jan 26, 2019

Having no background in linear Algebra made it difficult to complete the quizzes, assignments and exams. Even with the instruction (which was good) I found the hands on portions to be different from what was being explained in the videos. I will instead have to take the key concepts and do more research on my own to fully understand them.

교육 기관: Matt

•Feb 24, 2019

This course would be perfect if more elaboration on the maths required to complete the quizzes, was provided.

교육 기관: Alberto M

•Apr 04, 2019

Good material if you want to refresh your knowledge, poor programming assignment support/feedback

교육 기관: Carlos R T G R

•Mar 19, 2019

The videos need to be updated, there are quite some errors that are already identified...

교육 기관: Reed R

•Jul 14, 2018

The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e. (a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools. While I appreciate this goal and enjoyed the early foray into projection, I never felt the "a ha" moments I did as an undergrad in a class that used Gil Strang's "Introduction to Linear Algebra" (which I reread alongside this course as a supplement). The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course. It could be that I was just looking for a more complete treatment of the subject than the course ever intends to offer, but I strongly felt that with a bit of restructuring, that the subject could be presented primarily intuitively, but with a level of clarity and artfulness in its conclusion that will ensure that students remember the core concepts beyond when they remember its presentation.

교육 기관: Vignesh N M

•Sep 12, 2018

Transition from explanation of basic to advanced concepts could have been better. There was an assumption that few things was already know to the learner.

교육 기관: 丁榕

•Aug 30, 2018

I think the course is more suitable for those who have had comprehensive theoretical knowledge in linear algebra and intend to learn more about its practical use and its relevance to code.