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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: 8,603
Рецензии: 1,742

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.

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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151–175 из 1,738 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: Lance

23 нояб. 2020 г.

Course content is very useful and intuitive. I definitely feel much more confident with my Linear Algebra.

One thing I would suggest is to provide more exercises / practice quizzes on algebraic manipulation with matrices. I think this would immensely help in following the proofs and building a more solid intuition.

автор: Daniel G

29 мая 2019 г.

This course brilliantly delivered on each of its intended learning objectives in an engaging and non-threatening way - I would encourage anyone interested in this topic, regardless of their background. The course instructors are excellent, and the forum discussions are extremely helpful if/when you are ever stuck.

автор: Ashutosh M

6 мар. 2019 г.

The course is great for those who are new to machine learning and want to start from mathematics behind it. The course focuses on vector and matrices and how to solve System of Linear Equations using it. You will develop intuition of what matrix transformations are and how to use change in basis to your advantage.

автор: Jitesh J T

12 дек. 2019 г.

Superb lectures and lucid explanations of the topics make this course one of my favorites! The video quality was superb and the course content, assignments and degree of difficulty was wonderfully designed to test the skills. Would definitely attend more courses from Imperial college.

Thank you

Dr. Jitesh Tripathi

автор: Sharan S M

5 дек. 2019 г.

Great course. Really enjoyed it because the instructors teach well. Also, the practice quizzes are useful for understanding the content. I was able to do all the assignment thanks to all the practice that they have given. Great course and I recommend that anybody interested in machine learning take this course.

автор: Ashley Z

17 окт. 2019 г.

Really recommend to all who would like to dive into machine learning with some mathematical background in vectors, matrices and eigenstuff. The instructors are very good and the homework/programming assignments are manageable while giving good insights into the application of the formulas learned in the course.

автор: Maksim U

14 окт. 2018 г.

This is a great course. All explanations and examples are easy and useful, the tasks are challenging but solvable. Certain points of the course might be unclear for students with limited math knowledge, some tasks will make you look for extra info elsewhere. But all in all I would really recommend this course.

автор: Harsh D

6 мар. 2019 г.

Great Course, exceptional in every way, gives you practice drill down some of the concepts, and handy programming assignments that are fun to work with, while not a complete refresher the course is good enough to grasp essence of linear algebra to build intuitive math, rather than classical way of teaching.

автор: Joaquin R

5 нояб. 2018 г.

It has been a while since I took Linear Algebra in my undergraduate years. This course has improved my knowledge of Linear Algebra and most especially eigen theory. This will greatly enhance my understanding of Machine Learning. Thank you to the professors for imparting their knowledge of Linear Algebra.

автор: Sanjay B

1 нояб. 2020 г.

Good program for introduction level, almost no prior mathematical or programming skill required beyond high school level.

Designed to introduce key concepts on which further understanding can be built.

Nicely presented, and made interesting through quizzes es, assessments and simple programming assignments.

автор: Muhammad A

16 июля 2020 г.

I have studied linear algebra back in high school and in undergraduate studies which were full of hand calculated computation that makes me feel bore about L.A but thanks to this course which present Linear Algebra in a really beautiful that can help you built an intuition about this branch of mathematics

автор: Mingyang Z

6 сент. 2019 г.

Excellent course with clear instruction video to explain the concept of linear algebra. The assignment is relatively challenging to help practice the learnt concept. I wish I could learn more about some special characteristic of matrix, such as block matrix, and how to compute singular value decomposition.

автор: J. W

10 мая 2018 г.

I took Linear Algebra in undergrad nearly 20 years ago. The instructors for this course developed the inuition behind core concepts in such a way that it made the material very accessible and provided a great basis for further study using supplementary material. I am pleased with the overall presentation.

автор: Ronny A

10 июня 2018 г.

Excellent Linear Algebra refresher. I love it that this course distills and covers the core concepts in a very time efficient manner! Also, I am happy with the emphasis on images and graphs to develop intuition. The programming exercises such as Reflecting Bear and Page Rank have been curated well.

автор: Gyrdymov I

30 мая 2018 г.

The lecturers gave me robust intuition that lies behind almost all main processes in linear algebra. Also, the course has pretty good visualization side (bright, useful, clear and understandable images, schemes and plots are used in this course to provide better understanding of the main concepts).

автор: David B

16 февр. 2019 г.

The video approach to this course is really amazing. The visuals presented and the ease in understanding touch mathematical concepts made this course fantastic to take. Although I would have preferred more challenging quizzes and programming assignments the material taught was still world class.

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: 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.