Вернуться к Mathematics for Machine Learning: Linear Algebra

4.7

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Оценки: 4,820

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Рецензии: 879

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.

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.

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автор: 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.

автор: Eitan A

•Jan 13, 2020

As of this writing, I am almost done with week 4 of Mathematics for Machine Learning: Linear Algebra. The content of the course is excellent and professor David Dye's lectures are to be commended no doubt. The reason for my low rating is because the programming assignments are broken and that's really not acceptable for paid offering such as this. To clarify, at various points throughout this course, students are asked to complete a programming assignment. The student is presented with a button which says, "Open Notebook". The student is supposed to click this button and be redirected to a Jupyter Notebook (and interactive Python execution environment). Unfortunately, instead of being redirected, click on this button results in a "404 Not Found" error. There are various discussions in the class discussion forum regarding this issue (some months old), but no action has been taken to resolve this issue. Luckily, someone taking the course managed to find the programming assignments and posted them on google docs for others to use. I've been working these which is fine, but as I said, we're paying for these courses, someone should be resolving this.

автор: Anweshita D

•Jun 29, 2018

Your discussion forum really needs to be better. It seems to be the only place where any sort of doubt clearing can be done and very rarely have I seen TA's answering unless it's a grading issue. The problem with this sort of answering is that if any coding concepts are unclear, either they are solved by trial and error or after going through Google multiple times. And for a course that is paid for, I shouldn't have to make this much of an effort just to have my doubts cleared.

автор: Chika

•Jun 13, 2019

The videos were well structured, but the quiz sometimes were far more difficult than the practice questions in video. I had posted on forum but no comment nor reply. Quiz answers were not elaborate enough to understand after making mistakes. So I had to ask my father who's extremely good at maths many times, for explanations. Without hi help I might not have been able to understand as well. Need improvement.

автор: 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.

автор: Fernando B d M

•May 14, 2018

Like most of Coursera's courses there are no staff members available in the forums (which is extremely shameful for Coursera - repeating the same boring pattern over the years). Don't even try it if you have never seen linear algebra or python before. Otherwise, it's useful for practicing a few concepts or refreshing others.

автор: Mattia P

•Mar 30, 2018

Nice course, with many insights. Sometimes the topics are given too quickly, I would have rather preferred less arguments but discussed more thoroughly. Nevertheless, I think this is a good one, especially if you've already got some background and you're looking for some general content to build upon it using academic books.

автор: Alois H

•May 06, 2019

Teaching quality is good overall, except for a few jumps towards the end, where it's hard to follow. Quizzes and assignments well designed.

Unfortunately, and contrary to other courses I've taken, the forum seems completely un-monitored (as of May 2019), so don't expect much help from there.

автор: Marie-Luise K

•Jan 16, 2020

Overall, it was a good summary to understand linear algebra. To get into the topic, I had to read through additional material as the videos and tasks provided in this course were a little shallow to my liking. I, personally would have liked more applicable machine learning examples.

автор: Ilaria G

•Oct 24, 2019

I believe that the programming required in the assignments are not beginner level. I had never coded on Python before and I thought that there wasn't enough support on how to test my code before submitting, for example. On the other hand, the math topics were really interesting.

автор: Chakravarthy R

•Sep 16, 2019

It was too fast for me. I answered many questions just by chance. But i got an overview of the concepts like diagonalisation , inverse, transpose, basis, span , eigen and so on. I am hoping that i will build on this.

автор: POR M H

•Feb 01, 2020

I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm. There should be more explanation to how the math works or comes to its formula.

автор: 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.

автор: 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.

автор: chanhee

•Feb 25, 2020

It is good course for machine learning. But I didn't fully understand the page rank system with damping.

More explanation of damping is needed for the newbie.

автор: 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.

автор: Alexander D

•Aug 07, 2018

Not enough focus on how material connects to machine learning. A case study example would help, as would a very slow, detailed step-by-step illustration.

автор: 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.

автор: BT

•Dec 29, 2019

The Eigen system could have been better explained. The last quiz was too hard and the concepts required were not covered

автор: Aaron H

•Oct 17, 2019

Lot of the concepts seemed glossed over and could have used more guided practice and/or linkages to real world problems.

автор: Matt

•Feb 24, 2019

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

автор: shanmugha

•Dec 11, 2019

i expected a practical mathematic approach rather than only mathematical approach.but page rank algo is good

автор: Jared E

•May 26, 2018

Overall good, but some nasty difficulty with the programming assignments... especially the last one.

автор: Alberto M

•Apr 04, 2019

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

автор: Ahmad A R

•Oct 01, 2019

Repetitive/redundant questions in the assignment and minimal use of coding during the videos