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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: 9,099
Рецензии: 1,842

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

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.

EC
9 сент. 2019 г.

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.

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176–200 из 1,842 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: HBashanaE

17 июля 2020 г.

This is awesome. I have known the theory. But I didn't have the understanding. This course helps me to get the intuitive understanding of linear algebra. Highly recommend for anyone who needs to get the deeper understanding of linear algebra. Specially if you're not from mathematical backgrund

автор: Akshita B

11 нояб. 2018 г.

I feel this course is easy and challenging in its own way. It didn't overburden me but at the same time it made me feel that I am learning something every week. Also, they keep revising the concepts as they move forward so it helps retaining the concepts too. Cheers! I really liked the course.

автор: Shraavan S

10 нояб. 2018 г.

The interpretations given for matrix multiplication and change of basis are presented in simple terms which are easy to understand. I hadn't used Python earlier, but the programming assignments (especially the PageRank algorithm implementation) have motivated me to start learning the language.

автор: Moez B

19 июня 2019 г.

Excellent course with top-notch videos and instructors. I highly recommend it even if you are not going into data science. The approach to teaching eigenvalues and eigenvectors in particular is very helpful for any students struggling with these concepts in a classical linear algebra course.

автор: Omar H

1 янв. 2021 г.

Great course! This is exactly how education should be! Give us the intuition to what we are doing, relate it to real world problems and when is this knowledge useful and then get the opportunity to code that knowledge in python instead of wasting time with just hand calculations! Brilliant!

автор: Joshua G

24 февр. 2021 г.

Fantastic course providing a broad understanding of linear algebra for machine learning. The responsive quizzes and formal assessments provide a challenge and regular feedback on performance. Highly recommend taking their course for anyone who wants to develop the maths that underpins ML.

автор: Hermes J D R P

8 июня 2019 г.

A great course to learn the fundamentals of Linear Algebra for Machine Learning. The programming assignments in Python were the best part of the course because when I studied Algebra at my university I only did boring manual exercises. I recommend this course completely, you'll enjoy it.

автор: 刘佳欣

23 мая 2019 г.

This is an incredibly great course for linear algebra. Thank you so much for the neat and elegant explanation! Highly recommend it if you focus more on calculation without knowing the meaning behind matrices and vectors in your past linear algebra journey. Thanks a lot dear professors!!

автор: SUJITH V

8 сент. 2018 г.

This course has exceeded my expectations in some ways. I was just trying to get a refresher in basics of Linear Algebra. The intuitive understandings presented in the course were really helpful and gave me a better understanding of the concepts which I only learned mechanically before.

автор: Jack C

6 апр. 2018 г.

Great course, well presented videos and challenging but engaging content. Great high level view of linear algebra to give you a starting point for other courses. May be useful to have some machine learning knowledge before taking - Andrew Ng's course would serve as a good counterpoint.

автор: Aleix L M

28 нояб. 2019 г.

After taking this course I can safely say that I did not understand Linear Algebra before. This course introduces basic concepts useful for machine learning and it gives a very intuitive view on abstract concepts that I had trouble understanding before. I would totally recommend it.

автор: Satyajit S

18 мар. 2018 г.

Great introductory course. Linear Algebra is quite often the most poorly taught/understood subject in college mathematics.This course has a done a great job in stressing on the core concepts without focusing on the computational details which happens in typical linear algebra courses

автор: Alexander Z

25 авг. 2019 г.

Very much recommend this course for absolute beginners seeking to refresh/learn math required for machine learning.

Don't be afraid to start and focus on learning instead of going through the material.

Practice exercise you've done several times and return to your notes. Good luck!

автор: Alok N

14 апр. 2020 г.

Great course! Linear algebra is a very vast subject. This course helped me getting the idea of topics I need in machine learning algorithms. This course is very helpful in revisiting the linear algebra to those who have taken this subject in his/her college in very short time.

автор: Wade W

12 июля 2019 г.

It's a worth-taking course. But you'd better have some linear algebra background. Like me, a student in China, we learn all things with out geometric insight, it will be very difficult for you to take the course through out.

All in all, worth-taking. Give me many fresh airs.

автор: Dan L

29 сент. 2019 г.

I actually studied Maths at undergrad and was using this as a catchup after many years - it wasn't taught nearly anywhere near as well as this. More lecturers should focus on the concepts first, and then the formulae to give context. A great course, highly recommended!

автор: Anubhab G

6 июня 2018 г.

Well-paced, engaging and highly interesting course content. This course totally gives a new dimension to linear algebra. The fact that mathematical examples are implemented through programming exercises, really strengthens the concepts and makes it even more interesting.

автор: Maged F Y A

1 мая 2018 г.

I would like to thank the instructors for their exceptional work. They are teaching mathematics with the aid of visualizations, which is not common within ordinary math classes. This way assists students to understand the physical interpretation of mathematical concepts.

автор: Phuong A V

23 июля 2020 г.

It is quite hard course, especially coding.

the practice tests are very useful. Every test provides description which is very useful to review the lecture. Tests are challenging but if we make effort and invest time to think, read the instruction carefully, we can pass.

автор: Henry N

5 апр. 2020 г.

Lectures are well-paced (although I was familiar with basics of working with vectors and matrices from high school mathematics). The assignments and quizzes were pitched at the right difficulty, just hard enough to be a challenge but not so hard as to be disheartening.

автор: Pritam C

19 сент. 2020 г.

Eigenvalue &Eigenvector, Matrix & Inverse Matrix, The Gram–Schmidt process, Page RanK.

I was weak in maths and my background was not that strong, But I learned here how to tackle with

wonderful lecture tutorials

I want to apply ML in my research in electric power system

автор: Dariusz P G

10 мар. 2019 г.

What an excellent lecturer.

I just wish that my mathematics teacher at school had had a tenth of the ability to impart knowledge.

This is a fantastic course and I will be doing the specialization later when I get some free time.

Thank you for a fantastic course.

Dariusz

автор: Deleted A

23 окт. 2020 г.

It feels a bit intimidating at first!

Then you realize that it was a while ago since you needed this part of the brain.

Things might seem simple in some videos, but trust me it pays off in the end!

The last part of this specialisation requires you to be on your toes!

автор: Diogo J A P

22 июля 2019 г.

This is an awesome course! You probably were like me, with a foundation in maths shaky due to poor understanding of the underlying principles. This course re-centers math around intuition, making it much easier to understand and apply the concepts with confidence.