Вернуться к Mathematics for Machine Learning: Linear Algebra

4.7

звезд

Оценки: 7,495

•

Рецензии: 1,493

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....

Aug 26, 2018

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.

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.

Фильтр по:

автор: Ralph T

•May 04, 2019

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

•Aug 02, 2019

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

автор: Philip A

•May 16, 2019

Excellent Instruction

автор: Neel K

•May 10, 2020

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

•Nov 20, 2019

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

•Nov 27, 2019

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

•Aug 02, 2019

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

•May 05, 2019

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

•Aug 27, 2018

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

•Dec 12, 2018

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

автор: Rob E

•Mar 28, 2020

I learned a lot of valuable concepts in this course. But, the pedagogy is very poor in my opinion. The videos are taught by Professor Implicit, the notation is inconsistent and confusing, and I never saw even one response to questions from the instructors.

Seems this is for people who have a very strong math background even though it's marked as an introductory course. It took me several months to complete this because I had to go through almost all of the Khan Academy Linear Algebra course to understand.

Great concept and content. But, responses to student questions and better explanations would help a lot.

автор: Mesum R H

•Aug 26, 2018

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: Anonymous

•May 09, 2018

The content and the speed are not satisfactory.

The speed totally hampers the content, lots of things aren't explained especially after Sam took over in the last module.

Other than the first 2-3 intuition videos and the programming assignment nothing was good in the 5th module/week.

It was very very difficult to follow the page rank video. I still don't understand it. For eigen basis I had to refer to other material outside this course.

автор: Jorge N G

•May 02, 2018

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

•Dec 19, 2018

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

автор: PRAKHAR K

•Mar 11, 2018

Not good, concepts not explained clearly.

автор: Richard C

•Oct 16, 2018

Does not explain mathematics in videos

автор: Dmitry R

•Jan 13, 2019

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

•Jul 25, 2018

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.

автор: sitsawek s

•Sep 14, 2018

Quite difficult for learner who didn't know about linear algebra.It jump and few example and skip a lot of part for understand.But good for recall.

автор: Bram D

•Apr 29, 2020

In reviewing this course it is important to state what this course is and what it is not. It is not an in-depth formal introduction to the mathematics of linear algebra. For those who are looking for that, the course simply does not deliver. Secondly, while it is technically possible to complete this course without any beforehand knowledge of the topic, I think this would be incredibly challenging to do. Indeed, the course is not intended to be a first primer in linear algebra. The ease with which the instructors just juggle the cosine rule, or calculate the inverse of a 2 by 2 matrix indicates that they do assume you know such things. So also absolute beginners will be disappointed with this course. However, if you have had linear algebra in your past, and you are using this course to refresh your mind, it is absolutely brilliant. I can confidently say that nobody has ever presented this material to me in as intuitive a way. A well deserved five stars from me.

автор: John T S

•May 07, 2020

Above all I found this course well oriented toward becoming useful. The conscious avoidance of heavy mathematical description was a good choice for the online medium. As a learner, I suppose I might have learned better with a bit more... testing, I suppose is the word? To work through a few more examples? But actually, a few well-chose gulfs between the presented materials and (especially the last) testing materials brought some useful questions and explanations. The eigenvector materials are conceptually slippery. Maybe one more example to work through, with clumsier numbers? Although, maybe that would have been boring and confusing...

Which is why I'd give the course five stars. It makes complex material usefully simple, while acknowledging that some things are of necessity left out.

автор: Ritobrata G

•Jul 12, 2020

As a student with Physics background, I though that this course will be a quick recap for me. But was I in for a surprise! This course completely changed how I see matrices and vectors. The instruction videos were very edifying. The teachers were great. I am fervently thankful to Imperial College, London and Coursera for such a great course.

There could be some improvements- the assignments felt ambiguous at times. They were not clearly worded and what was expected was not clear. And there could be some very practical excercises in ML where the concepts I learned could be directly applied.

A special note- the instructors were great. Their method was well thought, cordial and the videos were very informative.

автор: Mia C

•Aug 02, 2020

I have just completed the first of the 3 courses in this series. First of all, I must thank Professor Dave for the excellent efforts he put into delivering those first 4-week of fun and excellent topics. You have made it so interesting to me that I would love to know more about linear algebra. (*** Not only that, you have taken us on a informative journey of shopping for apples, bananas, and carrots to some visits to bears! : ) : ) ***)

For week 5, Professor Sam had given me some excellent materials as well. I look forward to taking the second course in the very near future.

Both professors are so smart and clearly great teachers! I feel so lucky to have stumbled into this course!

THANK YOU BOTH!!

автор: Warul K S

•Jun 28, 2020

The representation of mathematical concepts as "tools" to solve practical problems was beautiful and enabling, the way the instructors build our intuition rather than providing us with a bland approach to simply solving mathematics questions was phenomenal, the structure of the course was definitely first class as one would expect from Imperial. We were guided through the assignments but not fed the answers, our understanding was tested and additionally built upon through each exercise. Overall, I would recommend this course to anyone studying the subject in college or desiring to build a solid mathematical foundation for machine learning or even simply to appreciate the beauty of mathematics.

- Искусственный интеллект для каждого
- Введение в TensorFlow
- Нейронные сети и глубокое обучение
- Алгоритмы, часть 1
- Алгоритмы, часть 2
- Машинное обучение
- Машинное обучение с использованием Python
- Машинное обучение с использованием Sas Viya
- Программирование на языке R
- Введение в программирование на MATLAB
- Анализ данных с Python
- Основы AWS: введение в облачные приложения
- Основы Google Cloud Platform
- Обеспечение надежности веб-сервисов
- Разговорный английский язык на профессиональном уровне
- Наука благополучия
- Научитесь учиться
- Финансовые рынки
- Проверка гипотез в здравоохранении
- Основы повседневного руководства

- Глубокое обучение
- Python для всех
- Наука о данных
- Прикладная наука о данных с Python
- Основы бизнеса
- Разработка архитектуры на платформе Google Cloud
- Инженерия данных на платформе Google Cloud
- От Excel до MySQL
- Продвинутое машинное обучение
- Математика в машинном обучении
- Беспилотные автомобили
- Блокчейн для организаций
- Бизнес-аналитика
- Навыки Excel для бизнеса
- Цифровой маркетинг
- Статистический анализ в здравоохранении на языке R
- Основы иммунологии
- Анатомия
- Управление инновациями и дизайн-мышление
- Основы позитивной психологии

- ИТ-поддержка Google
- Специалист IBM по привлечению клиентов
- Наука о данных IBM
- Прикладное управление проектами
- Профессиональная сертификация IBM в области прикладного ИИ
- Машинное обучение для Analytics
- Пространственный анализ данных и визуализация
- Проектирование и управление в строительстве
- Педагогический дизайн