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Вернуться к Mathematics for Machine Learning: Linear Algebra

Отзывы учащихся о курсе Mathematics for Machine Learning: Linear Algebra от партнера Имперский колледж Лондона

4.7
звезд
Оценки: 8,619
Рецензии: 1,747

О курсе

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....

Лучшие рецензии

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.

PL
25 авг. 2018 г.

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.

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101–125 из 1,742 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: ChristopherKing

22 мар. 2018 г.

This is such a great course for student already have background about college level linear algebra knowledge, but don't know the under relationship among those terminologies. For instance, after this course I finally know what is dot product means, what is eigen characteristics. The content of this course are well prepared, this is such a masterpiece from Imperial College London. Thanks to all stuff behind this course.

автор: Srutimala D

12 мая 2020 г.

The connection between machine learning ad vectors got clearer as the course moved ahead. The quizes are detailed and requires actual understanding of the concept which is not hard to grasp once you pay attention to the lecturers who themselves are so passionate about the subject, makes me excited to learn too. I can say, I finally, after leaving high school, have understood high school maths and it's applications.

автор: Ashish D S

9 апр. 2018 г.

This is excellent course on Linear Algebra. The best part of this course is, lectures focus on the physical interpretation of the topics rather than making you practice formulae without understanding. This course helped me refresh my Linear Algebra concepts and also helped me better understand change of basis and Eigen related concepts.

Many many thanks to professors for excellent course design and presentation.

автор: Ritesh S

28 июня 2020 г.

No one can hate mathematics. The only reason you hate it or don't visualize it is because you never had an instructor who could do it. But, this course solves this problem with beautiful designed course content and intuitive quizzes that help you understand the underlying concepts on a broader perspective. Want to understand and visualize the basics of Linear Algebra used in ML, this is the course to apply to.

автор: Rajakrishnan S

29 мая 2020 г.

awesome content with excellent pace. no bullshit during lectures. only place for improvement would be to give relevant content in readings as the course feels just of videos and less reading materials for reference. Ofcourse ,one can look up in textbooks , but giving the reading materials in the course will improve the readability and findability and will be according to the lecture content. thanks for asking!

автор: Vincent L

9 июня 2018 г.

I took this course as a review for my data science curriculum. Previously, I was having trouble recalling the details of matrix arithmetic which was making it hard for me to get a deeper understanding of machine learning. After doing this course, you should have no trouble following along. For those already familiar with the material, it should take about 1-2 weeks to complete if working at a leisurely pace.

автор: Thuy T N

7 авг. 2020 г.

This is my first encounter with Linear Algebra and surely the course has been extremely helpful beginner-friendly. I recommend investing in practical mathematics courses as this specialization if you are new to machine learning field. You will be equipped with enough math background and should feel confident to enter more technical machine learning/deep learning courses.

A truly fundamental stepping stone!

автор: Joseph F

20 июня 2019 г.

This course is perfect for many including those, like myself, who haven't seen this for 20+ years. I can imagine that it would be helpful to have, at least, a proclivity towards programming if you do not have familiarity with a programming language (at least course comments tend to reflect this).

For those experienced with coding, no difficulty will be encountered, as focus here is trivial (numpy libs).

автор: Digeesh J

21 мая 2020 г.

This course is good for everyone, as rather than diving deep in the paper pen model, the intuition is taught. For those who already know Linear Algebra, it is best to take this course to understand, what you are writing and what each formulae is doing. For those who dont know anything about Linear Algebra, but is interested in Machine Learning, do this course to atleast have the intuition behind it.

автор: Sertan A

12 июля 2020 г.

It's a very fundamental necessity for getting a hold on vectors and functions within a big programs. I have finished Mr. David Dye's part and he is an extraordinary teacher! I know Mr. Samuel Cooper from the calculus course and I assume his course will be quite awesome as well!! Thank you for making this available for people who need to upgrade their understanding, I respect this global mission!!!

автор: Mohiddin S

1 мая 2020 г.

I am very grateful to the in structures and the platform providers who designed the the course to enrich the knowledge of mathematics in good manor. From this course I learned a lot. Being a mathematician I feel that there is a need to change form traditional teaching to technological oriented teaching. This course helped me in finding such a path.

Thanking you

regards

Dr.Shaik Mohiddin Shaw

автор: Edisson O A C

29 апр. 2020 г.

Great course as a starter to understand the basis of linear algebra in machine learning. I had already taken a course of linear algebra as an undergrad but this course really opened my view of the applications and importance of some concepts I understood in a merely abstract way. The instructors are not only excellent in their explanations but you can also feel their interest for the subject.

автор: Lisa M

7 апр. 2018 г.

This was a fantastic course. I'm new to linear algebra, so it was bit intimidating even signing up (!) - but the lecturers were really, really good about explaining all concepts from the ground up so it was always possible to visualize and extrapolate from solid foundations. For me it was a stretch each week, but in a good way: very challenging, but achievable with enough planning and effort.

автор: Wasif S

3 авг. 2020 г.

This course for me was meant to be a habit-refiner but ended up being thinking of more into the depths of the world around. Very good course. The quiz & assignments are really good. Although some of the details are missing from particular sections, I think it will grow over time. As a Machine Learner's intro to perspective, this is really been a decent exercise to flex yourself. Goodluck.

автор: Ying T

9 мар. 2018 г.

An awesome course with high quality video lectures!! I will recommend this course to anyone who's looking for a refresher or quick pick-up on linear algebra. The homework's compatible with the materials and is quite interesting. The lecturer also did a good job on explaining critical concepts with easy but good examples. I'm looking forward to more similar courses from Imperial College.

автор: Jayant V

29 мар. 2018 г.

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.

автор: SHOUNAK B

12 июля 2020 г.

I personally loved this course as it changed my outlook and perspective towards Mathematics in computing and as a whole. I really enjoyed taking this course. It got me into some difficult assignments but the joy of solving it after lots of brainstorming and discussing with peers was awesome experience. I look forward towards developing myself and gaining knowledge with these courses.

автор: Stefan R

28 апр. 2020 г.

It is usefull and good course. It does require at least some familiarity with the concept or otherwise you will spend hours trying to understand how things happen, but lecturers have overall given great insights in how things work and tried to simpliify as much as possible.

Maybe would be usefull for course creator to add some optional basics tests on the topic, for total beginers.

автор: Huy M

11 мар. 2019 г.

I've only done half of the course but I already know this course is one of the best on Coursera! Complex concepts in mathematics are broken down into simple terms. The professor also clearly stated what those concepts are used for in practical, which certainly help learners have a clear idea of why they are learning this course. Very exciting every time I click onto new lessons!

автор: Hardik S

20 июня 2020 г.

Not being from a Mathematics Background, one surely need best tutor I guess for understanding Mathematics that's required in Machine Learning/ Data Science. Both the tutor Sam Cooper and David Dye amazingly Explained the topics and I'm happy to have completed the Linear Algerbra Course and now moving towards other part of the course i.e Course 2 Multivariate Calculus.

автор: Ramon M T

20 авг. 2019 г.

Excellent Course, I remembered the linear algebra that I saw in school more than 26 years ago (I studied applied mathematics and switched to Actuaria), but now with examples related to DataScience.

As observation.

For someone who has not programmed in some language the exercises can be challenging, but they are always very intuitive if the example steps are performed.

автор: Eric H

13 нояб. 2020 г.

Getting back into math after taking about 12 years off, and this was a great dive back in. I got a lot out of working the math out by hand for a few examples. There were some gaps in my understanding (when calculating eigenvectors, we need to solve for x1 and x2, but they don't have to be 0). Overall it was a great course and I'll be referring to my notes regularly!

автор: Badri A

1 мая 2020 г.

At first, I was kinda of afraid of Math in general and Linear algebra in particular, but after taking this course, I am satisfied with it.

A special thanks to the instructors and all the people behind this course, for making thing simple and comprehensible, and at the same time, hit the target. Looking forward to keep learning and carry on with this specialization !

автор: RHEA R B

20 мая 2020 г.

This course was very informative . Having learnt to solve most of this problems by hand in under-graduation , this course helped me to code these hand-worked problems . Additionally I was able to understand and visualize what the problems actually do . I highly recommend this course for anyone who is looking to learn or advance their career in machine learning .

автор: Art P

8 июня 2018 г.

This course was of high quality, was very helpful in explaining some key concepts and I appreciated the instructors energy and humor. My only complaint about the course is that some of the quizzes and homework assignments felt significantly more challenging than what was covered in the lessons; however, the discussion forums proved helpful in closing this gap.