Вернуться к Mathematics for Machine Learning: Linear Algebra

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Оценки: 9,547

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Рецензии: 1,927

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....

CS

31 мар. 2018 г.

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.

NS

22 дек. 2018 г.

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.

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автор: Karandeep

•9 окт. 2020 г.

This course is great for those who want to understand the geometric meaning of linear algebra. Really loved the course videos and quizzes. Just one suggestion - Coding assignments should be bit more challenging as this course is targeted around ML, maybe some small Kaggle like project at the end of course.

автор: Siddhant J

•13 апр. 2020 г.

Excellent, crisp and to the point. Instructors made the concepts way to easy to understand. Enjoyed my time learning from them and ofcourse relevant material was provided.

автор: David S

•1 янв. 2021 г.

A good value, well organized, with many exercises for practice. Effectively uses visuals, and contains the occasional very creative example.

Some caveats

a) this course is not for the absolute beginner. You'll need secondary / high school math, and basic familiarity with python

b) understanding linear algebra at this level is a second year full semester course at university. So if you want to understand the concepts - rather than just get the certificate - be prepared to use outside resources and invest considerably more time than advertised. Some linear algebra topics are skipped (cross product), and others are not well integrated into the course (Einstein summation)

c) while linear algebra is central to understanding machine learning, there are very few machine learning applications in this course.

And finally a small annoyance: I wish the instructors would get out of the way of the whiteboard at the end, so I could get a screen capture.

Overall, a worthwhile course.

D

автор: khaled W S

•25 мар. 2019 г.

totally enjoyed it. requires a bit of side research as any online course would. some of the quizzes were not directly related to the video that preceded them as one would expect. However, a fun course and covers a lot of important basics for it's relatively short duration.

автор: JUNXIANG Z

•17 мая 2019 г.

This course reviews the essential concept of linear algebra in the context of machine learning. However, it would be much better if it provided more optional exercise and reading materials.

автор: Ralph T

•4 мая 2019 г.

decent course. It gives a good enough background to understand the mathematics necessities of many areas of data science. could be more thorough and dive deeper into some of the content.

автор: Mark J T

•2 авг. 2019 г.

Good course because it shows how to understand geometrically, things that I had hitherto only understood computationally.

автор: Philip A

•16 мая 2019 г.

Excellent Instruction

автор: Neel K

•10 мая 2020 г.

For the most part, I enjoyed this course. Most of the math explained is fairly easy to understand. They cover the fundamentals of linear algebra, and provide plenty of assignments and practice exercises to test your knowledge. However, some of the video explanations are extremely confusing and feel rushed. For example, some videos in Week 4 and 5 like Reflecting in a plane using Gram-Schmidt and the PageRank algorithm were so hard to understand that I had to learn about them from elsewhere on the internet (I used MIT OCW a lot). This isn't very convenient, especially if you're paying for the course. Furthermore, I felt like more videos explaining the applications of linear algebra in machine learning could've been made, and the ones that were already made could've been made in more detail (for example, the term 'span' was never formally explained). Lastly, I would've loved it if there was another week dedicated solely to introduce the coding bit, because it's really difficult and takes a while if you have little or no prior experience in python. All in all though, I enjoyed this course, and I would recommend trying to complete both Linear Algebra and Multivariate Calculus in one month, because it's not worth paying more than that.

автор: Maytat L

•20 нояб. 2019 г.

Challenging course. Much more difficult that I expected. It took me 7-9 hours a week. The overall course material itself was good building-blocks to further understand application of machine learning. However, explanation in some topics should have more detailed explanation and examples to further understand the concept. There were many times, I need to re-watch each video over and over again, paused it, and figured things out on my own. The programming assignments were the most challenging task. I just began to learn Python and found it very difficult because there were so many codes I haven't learnt before. I think for those who has not learnt Python at all may find really really difficult to pass the assignments.

автор: Peter B H

•26 нояб. 2019 г.

The content was good, but a couple of times what was said didn't gel with what was being drawn/written/done. Since I'm learning, this took me longer to double check when I misunderstood something whether it was the concept or a mistake in the delivery.

автор: Pedro C O R

•1 авг. 2019 г.

The topics could be improved in the way they are presented. I always had to search for additional material.

However, the course is okay, it could be better, the forum is not that active, and some assignments are good.

автор: kai k

•5 мая 2019 г.

many of the activities are excellent, but videos hard to follow along to at times - play them at 0.75 speed if you can. Also, the faculty is not super responsive it seems on discussion boards creating some confusion

автор: Girisha D D S

•27 авг. 2018 г.

Although the course content is good, I feel it could have been done better. I enjoyed the multivariate calculus course compared to this course.

автор: Maximilian P

•12 дек. 2018 г.

Some exercises are completely incoherent to the preceding videos, which makes it very difficult to solve them. very frustrating

автор: Dr. V N R

•9 дек. 2020 г.

Assignment makes frustration and not able to concentrate on teaching content

автор: Mesum R H

•26 авг. 2018 г.

The course tries to cover every edge of Linear Algebra but fails to integrate each step with what relationship it has with Machine Learning. Core Formulas and Mathematical derivations are shoved down from throat without any respect for learners from non-engineering or computer science background. Other than week 1,2 rest was completely case study or example less UN-intuitive lectures of matrix formations and transformations. Needs a severe revamp with better examples and broader picture.

автор: Jorge N

•2 мая 2018 г.

Mainly explains how to operate with matrices and vectors. Not how to use those in machine learning. If you expect to have a clear view of the usefulness of eigenvectors and eigenvalues in machine learning, this is not your course.

автор: Arno D

•19 дек. 2018 г.

Some concepts were not clearly explained and there were a lot of issues with assignment grading working properly.

автор: prakhar k

•11 мар. 2018 г.

Not good, concepts not explained clearly.

автор: Richard C

•16 окт. 2018 г.

Does not explain mathematics in videos

автор: MARGARET P

•31 июля 2020 г.

Why can't I give this course ZERO stars? Because that is what this course deserves.

The first course in the specialization was a train wreck. For starters, the videos were heavy on theory and light on examples, so when it came time to do the practice exams, each student needed to go to outside sources to learn, from the top, what they needed to do to complete the questions. This expectation is unacceptable. Secondly, no mention in the course information, videos, etc. was there any indication that there was coding. These coding assignments are delivered with no hint given as to what we would need to do, how, and why, which is entirely unacceptable. Lastly, the course creators are available nowhere. There are hundreds of questions on the forums for each week of each course, with not one answer coming from any of the course creators. I even went out of my way to find the email for the leading course creator and ask for additional resources/help but received zero response in return. I have been an avid supporter of Coursera for a long while now, but this specialization is terrible enough that I would consider never utilizing this site again. Mathematics for Machine Learning is an embarrassment to the entire service and devalues all of the work individuals have put into learning through this platform. It does this by diminishing the quality of the certificate by demeaning the level of competence acquired upon completion. If I were in charge of content, I would remove this specialization as well as thoroughly review all content published by the same institution. David Dye and the Imperial College of Londen should be ashamed.v

автор: Mary B

•29 янв. 2021 г.

I only completed three out of the five weeks of this course. Too many of the lessons were just a source of frustration for me. The instructor doesn't explain things very well. For example, with change in vector basis, he walked us through using the dot product and scalar values, but then added them up. Nowhere did he say the last part was just a check, and it had me confused for quite a long time. Then, with Einstein's Summation Convention, he doesn't really explain the subscripts and what rules there are for their use. Plus, it's hard to follow along because he says the math out loud, then just writes down the answer. Since this is new to me, it would be good to see it written out, like | (1/2)(-2) + (-1)(4) |. Far too often, I had to rely on other resources to get enough of an understanding to complete the quizzes. By the fourth week, I started just skipping to the quiz and finding other resources to teach me how to solve the problems. Then, I decided to just give up entirely. And finally, there were issues with the auto-grader. With one, I needed to write out the values as 2.0 instead of 2, but there was no mention of needing this precision. With another, it was A[3, 0] (with a space) instead of A[3,0] (without a space), even though the provided code used A[1,0] (without a space).

автор: Dmitry R

•13 янв. 2019 г.

Authors try to teach babies. Might be good, it is hard to judge for me as I know linear algebra. Definitely boring to me. For example 3Blue1Brown (which they reference btw) is ingenious in my opinion, so it might be not me who is the problem.

But the quizzes just don't make sense! The ones where solving problems involved might have 2 numerically right answers but only one of two is treated as the right. And there are just idiotic or not covered in lectures answers for quizzes without problems.

автор: Patrick B J

•25 июля 2018 г.

Hands down the worst course I've ever taken in my life! Poorly put together and extremely short videos that don't provide an adequate amount of knowledge especially in relationship to the given quizzes. I truly hope this course is removed.

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