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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: 2,579
Рецензии: 643

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.

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601–625 из 637 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Kimberely C

27 дек. 2019 г.

Definitely, not for beginners. Just as bad as the last one. They need to have more examples, which walk you through the ones like they give you on the homework as well as an example of how to do Python.

автор: Gurrapu N

9 апр. 2020 г.

There is hardly any co-relation between videos and assignments, while the lectures were at high school level but the assignments were at graduate level. It is high time to revise the course contents.

автор: Marcin

19 авг. 2018 г.

By far the worst online course that I've ever done. Assignments require a lot of experience in Python, which is not communicated upfront. At the same time, staff doesn't provide any actual support.

автор: Danielius K

24 сент. 2019 г.

You will spend most of your time lost.

Quizes are not clear and ill-prepared.

You will need to spend a lot of time looking for material outside of the course to actually make progress.

автор: Saransh G

28 апр. 2020 г.

1. Not intuitive like first two programs

2. The assignments sometimes jumped concepts and were not cohesive

3. The in-lecture problems seemed rushed through

автор: Tai J Y

16 нояб. 2019 г.

This course is not like other two, which explain much clearly. When I do the practice quiz and coding, I resort to find other help on the Internet.

автор: Vibhutesh K S

17 мая 2019 г.

This course is really bad and extremely hard to follow. Previous two courses were executed very well, teaching quality in this is poor.

автор: Alejandro T R

2 авг. 2020 г.

Worst of the three courses. I learned much more on the internet because of the lack of examples or explanation. Just not worth it.

автор: Ananya G

28 дек. 2019 г.

I did not register in this course to have some person read out the textbooks or dictate the derivations in the lecture videos.

автор: Sherif B

3 мая 2021 г.

Very bad experience, skips steps, does not reflect on intuitions like other courses in the specializations, monotonous.

автор: Yap C Y

7 мар. 2021 г.

Explanations need to be clearer. Efforts are needed in explaining the details of every components in this course.

автор: Michael K

30 нояб. 2020 г.

Lowest rating as the third course was absolutely poor. Low quality and in some way non-existent instruction.

автор: Nithin K

5 июня 2018 г.

Too conceptual and theoretical making it difficult to understand. Examples would have helped a lot.

автор: Kamol N

28 янв. 2020 г.

very very bad course! Assignments and quizzes made as shit. NO answers. Worth NOTHING!

автор: Sairam K

9 янв. 2021 г.

The course videos provide insufficient and/or misleading context for the assignments.

автор: TUSHAR K

19 июля 2020 г.

Previous Two Courses were better in terms of both assignments and teaching.

автор: Nathaniel F

14 мар. 2021 г.

I think there are broken graded assessment in week 4 'test_normalization'

автор: Siddharth S

4 июня 2020 г.

Very Poor when compared to previous two courses of this specialization.

автор: Saeif A

1 янв. 2020 г.

This course was a disaster for me. The first two were great though.

автор: Jared E

25 авг. 2018 г.

Impossible to do without apparently an indepth knowledge of python.

автор: Soumitri C

15 дек. 2020 г.

okayish teaching but grading system is absolute rubbish in Week4

автор: Aditya P

4 июля 2020 г.

Very poor teaching and overall it's the worst course I've taken

автор: Ahmad O

27 авг. 2020 г.

Very bad explanation. The assignments need more instructions.

автор: Aurel N

5 июля 2020 г.

k-NN assignment is full of errors and no proper explanations.

автор: Kapeesh V

17 апр. 2021 г.

Week 4 Assignment is not constructed properly.