Chevron Left
Вернуться к Mathematics for Machine Learning: PCA

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

Оценки: 2,688
Рецензии: 673

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

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.

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:

401–425 из 669 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Liang S

9 июля 2018 г.

Teaching pacing is good, and clear in explanation. It will be good if there are some examples about how we should apply all these theories to some real problems.

автор: Kevin E

27 авг. 2020 г.

Overall the course was great. The only thing was that there was a lot I didn't understand from the videos. The recommended textbook resource was a great help.

автор: Ezequiel P

26 сент. 2020 г.

The other two courses were much more didactic. And there were some bugs in these courses assignments... But, overall, it was a great course on the subject

автор: Mohamed B

27 окт. 2019 г.

I learned a lot in this course, though the last week was somehow hurried and the lecturer didn't spend enough time to piece the whole stuff together.

автор: Jorge L C T

5 сент. 2021 г.

The explanation of the model is very precise but there are unnecesary comments for PCA omit the comments related with std in the final assignment

автор: Rok Z

5 февр. 2020 г.

A different course than the previous 2.

Much harder - as you have to actually know some Python tricks.

But I guess it's the same in a real world.

автор: Jordan V

23 авг. 2019 г.

Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.

автор: Kevin G

14 янв. 2020 г.

Felt like explanations in this course were a bit confusing, but otherwise, it was a very interesting course. Thank you so much for doing this.

автор: Helena S

28 февр. 2020 г.

The final Notebook contains some errors (Xbar instead of X, passed as an argument). Otherwise a very well organized course. Thanks a lot!

автор: Giri G

7 июня 2019 г.

This was a very hard course for me. But I think the instructor has done the best possible he can with presenting and explaining the course

автор: Leon T

10 июля 2020 г.

Jupyter notebook assignments are in desperate need of attention! Very buggy or non-intuitive for the scope of material in span of time.

автор: Christine W

13 авг. 2018 г.

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

автор: Shaiman Z S

30 апр. 2020 г.

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

автор: Karan S

1 авг. 2020 г.

Focus a bit more on PCA in week 4, week 1 was not very informative and should be assumed as required knowledge for the course

автор: Hilmi E

20 апр. 2020 г.

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

автор: Voravich C

21 окт. 2019 г.

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

автор: Phuong N

17 окт. 2018 г.

That's a great online courses can help people have enough background to break into Machine Learning or Data science

автор: Ananthesh J S

16 июня 2018 г.

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

автор: Manuel I

7 июля 2018 г.

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

автор: Shraavan S

4 мар. 2019 г.

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

автор: Andrew D

2 июня 2019 г.

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

автор: Neelam U

23 сент. 2020 г.

The programming assignments were quite challenging. Some part of the course can discuss this aspect as well.

автор: Paulo N A J

18 авг. 2020 г.

It is a good course with hard programming, but the assignments could be improved. The forum helps a lot.

автор: Ibon U E

7 янв. 2020 г.

The derivations of some concepts have been more vague compared to other courses in this specialization.

автор: Max W

19 апр. 2020 г.

Very challenging, could have used a few more videos to really explain or give a few more examples