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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: 2,544
Рецензии: 629

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

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.

NS
18 июня 2020 г.

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

Фильтр по:

526–550 из 624 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Michalis D

22 июля 2019 г.

After having done the first two parts of the specialization, I am afraid this one didn't stand up to the high quality bar the previous two had set. The programming assignments are unnecessarily long and complex and the overall material is not as engaging, connected and concise. I might give it a good rating as a standalone but now I can't avoid comparing it to the other two parts of the specialization.

автор: Ricardo F

4 мар. 2021 г.

This course is a great departure from the premise of bringing Mathematics and ML together. The instructor basically throws dozens of calculations, Math for the sake of Math. No intuition is gained, either. The instructor is only preoccupied with writing down the calculations. You either "swallow" all the Algebra or not. I'd suggest the complete reformulation of the videos.

автор: Daniel A

9 мая 2020 г.

Compared to the first modules in this series, the instructor explains almost none of the intuitions behind the maths and will skip over large essential pieces required to complete assignments and quizzes. It assumes a wide knowledge of programming and broader maths that was handled significantly better in the earlier courses.

автор: NamTPSE150004

11 февр. 2021 г.

The explanation of the course is hard to understand. Some misunderstanding. I have to study on youtube or somewhere to pass this course. The videos in the course are lack information. 2 stars because of the PCA assignment helping 1 plus star for the course. The two courses previous in this specialization are good.

автор: Xiaoxiao L

4 янв. 2021 г.

This is the least effective course among the three courses in this specialization. The reading materials have no context. People who have not been around those math symbols have no idea what the reading materials mean. They are not intuitive at all. The design of the assignments are poor as well.

автор: Alois H

18 февр. 2021 г.

This course has been a nightmare. Dense and obscure lectures, "challenging" assignments asking for things that haven't been properly taught in the lectures and often unclear instructions.

Yes, some useful concepts are taught but overall it's rather a waste of time.

автор: Daniel U

27 сент. 2018 г.

Programming assignments seemed to be written from a completely different direction, and instructions are vague and misleading. (The math assignments were not so bad.) There was no staff or mrntor engagement in the forums during the period of the course.

автор: amit s

8 февр. 2019 г.

Unlike the prior courses in the series, topics not clearly explained and brought too sudden. Also none of calculations shown completely, instructor just wrote results in the end. Due to all these reason I was not able to finish the course.

автор: Kevin L

11 сент. 2018 г.

The course assignments could be improved dramatically, though the course itself has very good content if you want to have a taste of how linear algebra (predominantly) can be implemented to solve machine learning problems.

автор: shashank s

17 февр. 2020 г.

First two courses in this series are great but not this one. Lectures and exercises are not related. I do not feel like I have totally understood PCA. Was able to complete the final assignment thanks to the internet.

автор: Bohdan K V

13 авг. 2020 г.

The course is awful, it's nothing compare to previous 2 courses. It has a lot of errors in assignments objectives and quizzes! The explanation is complete crap! I'm wondering how was it even allowed on Coursera?!

автор: Ivo R

16 нояб. 2019 г.

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

автор: raghu c b

4 апр. 2020 г.

Needs to demo a little bit of code owing to the complexity of the course content.Lectures gives just a high level understanding only. Assignments are taking far more complicated than expected.

автор: Paulo H S G

27 нояб. 2020 г.

Even though the videos and quizzes are well produced and informative, the assignments are so poorly designed that they can only bring about some frustration with the learning process.

автор: Anjali s

23 авг. 2020 г.

Faced a lot of problems in exercises. Don't feel that i have completely understood the concepts. This course can be made more learner friendly with better explanations.

автор: vignesh n

12 сент. 2018 г.

Explaination of many things are skipped, assumption was made by the instructor that lot of things were already known by the learner. It could have been much better.

автор: Maksim S

25 мар. 2020 г.

The difficulty of the course is inadequate and the pace is not balanced. Requires a lot of search for additional resources to understand materials. I cancelled.

автор: Kovendhan V

11 июля 2020 г.

After first two amazing courses in this specialisation, third course was a huge let down. One skill I learnt from this last course is patience.

автор: Martin H

8 дек. 2019 г.

Lack of examples to clarify abstract concepts. Big contrast in quality compared to the other courses in this specialization.

автор: Dipto H

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: Kirill T

26 июля 2020 г.

Way worse than the previous courses. Lacks explanations