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Вернуться к Mathematics for Machine Learning: PCA

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

4.0
звезд
Оценки: 2,668
Рецензии: 671

О курсе

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Лучшие рецензии

WS
6 июля 2021 г.

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

JS
16 июля 2018 г.

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

Фильтр по:

601–625 из 668 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Jamiul H D

7 авг. 2020 г.

Poor explanation by the instructor. Previous ones were very helpful. I didn't understand many topics well

автор: Lavanith T

21 авг. 2020 г.

Everything is okay but there is a huge drawback with the programming explanation part.

автор: Xiao L

3 июня 2019 г.

very wired assignment, a lot of error in template code. The concept is not clear.

автор: Sai M B

3 авг. 2020 г.

The lectures were not clear. I had to use other sources to understand lectures.

автор: Pawan K S

20 июня 2020 г.

This course was the hardest I encountered in this specialisation.

автор: Mohamed A H

18 авг. 2021 г.

it was not clear alot of the time and it was really hard

автор: Kirill T

26 июля 2020 г.

Way worse than the previous courses. Lacks explanations

автор: Kevin O

27 мар. 2021 г.

Really interesting topic but not nearly enough detail.

автор: Amr F M R

22 сент. 2020 г.

I think course material was not explained well at all.

автор: Timothy M

22 апр. 2021 г.

The lectures and assignments did not synergize well.

автор: Desikan S

23 сент. 2019 г.

Need to improve the content and delivery of content.

автор: Mohammed A A

19 июля 2020 г.

the course is too shallow with difficult code exame

автор: Scoodood

28 июля 2018 г.

Video lecture not as intuitive as previous courses.

автор: Michael B

21 нояб. 2019 г.

Programming assignments not well explained

автор: youssef s

27 июля 2020 г.

very poor explanation of things

автор: Murilo F S

24 янв. 2021 г.

not good teacher :Z

автор: Salah E

4 авг. 2020 г.

again too hard

автор: Alan

4 авг. 2020 г.

Very disappointing compared to the other courses. Recommend a complete revision of the course materials. Quizzes often had nothing to do with the preceding video. I worked through a week two quiz using the extensive notes in the discussion forum and by searching the internet. The next lecture proceeded in the same vein: the instructor failed to cover the material in video leaving me to figure out what the material was and then figure out how to find that material on the internet or in reference books. At that point, it just was not worth the time to take the course.

автор: 周玮晨

8 июня 2018 г.

This course is far far far behind my expectations.The other two course in the specializition is fantastic. There is no visualization in this course, Instructor is always doing his algebra, concepts are poorly explained. I can't understand a lot of concepts in this course because of my poor math background.But why do i take ths course if i have a solid background in math? Programming assignments is not difficult but hard to complete because of vaguely clarification.Plenty of time wasted to find what should i do, its' really frustrating.

автор: Hannah Q

26 апр. 2021 г.

This is the worst course among 3 courses in this math for machine learning specialization, but this is also the most important one as it comprises the other 2 courses. the homework is not well designed, the lecture is not taught enough to finish the homework. the last coding assignment has so many errors. and TA is never available, there are just a group of people tried really hard to help each other in the discussion forum, and please read the discussion forum every one is confused and suffering from your this bad course.

автор: Adarsh R K S

21 февр. 2021 г.

The first two courses in the entire specialization were good. The PCA course was then suddenly so complicated and assumed significant matrix knowledge which was not taught in the previous courses. also, the course kept introducing concepts into the material without any explanation of where this came from and the why behind it. the lecturer needs to understand that most people taking this course are not mathematicians by profession and so we would have learnt better if the PCA was kept at a basic level.

автор: Srudeep K

6 апр. 2021 г.

Alot of the material are to be referred, read and understood from sources outside of the course which is frustrating. There is lack of continuation from first two courses (Linear algebra & Multivariate calculus). At times, lecturer explains concepts without giving any background. Tests front run the course, meaning some questions you get in tests are taught in the video just after the test. I find better resources elsewhere online to understand PCA much better than wasting few days on this course.

автор: Hossameldein E

9 апр. 2021 г.

The other Two courses are great, really great. But this one is a disaster.

Most of the the first 3 weeks i google the theory again to understand the problems in the quizzes.

The 4th week feels like something from a different course.

This videos are dull . a lot of time just reading the equations without trying to know what is it about in the real life.

please reconsider re-constructing this course. it's really sad that after the two great courses it ends up with this.

автор: Rameses

18 авг. 2020 г.

This has to be one of most nightmarish, ugly, courses I have taken on the Coursera platform. Lousy, boring instructor, assignments that are so full of bugs that even the staff cannot resolve the issues. Add to that very low participation from the mentors and teaching staff in responding to student concerns and questions.

Hey Marc, teaching staff and Imperial College. Get your act together!!

IOW This course sucks big time! Take it at your own risk