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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

Оценки: 2,601
Рецензии: 646

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

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.

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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401–425 из 641 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Ashok B B

6 февр. 2020 г.

Course was challenging , but learned the maths behind PCA,

автор: Cesar A P C J

23 дек. 2018 г.

Good content, just need to fix the assignments' platform.

автор: Dave D

30 мая 2020 г.

This course was a fair overview of a very complex topic.

автор: ADITYA K

13 мая 2020 г.

It is very informative and hands-on based Course for PCA

автор: Md. S B S

4 мая 2020 г.

Not as good as the other two courses..but interesting!

автор: Sharon P

24 сент. 2018 г.

Mathematically challenging, but satisfying in the end.

автор: Paulo Y C

11 февр. 2019 г.

great material but explanation are a little bit messy

автор: wdelawed

21 февр. 2021 г.

Good course, but requires mathematical background

автор: taeha k

27 июля 2019 г.

Good but slightly less deeper than the other two

автор: Eddery L

24 мая 2019 г.

The instructor is great. HW setup sucks though.

автор: Manish C

6 мая 2020 г.

Best course for machine learning enthusiast

автор: Thijs S

28 сент. 2020 г.

The last assignment could use improvement.

автор: J N B P

10 сент. 2020 г.

Good for intermediates in linear algebra.

автор: Romesh M P

16 янв. 2020 г.

Too much non-video lectures (lot to read)

автор: Tanmoy S

13 июля 2020 г.

The last course could have been better.

автор: Kailash Y

9 июля 2020 г.

Challenging but in a good way.

автор: Muhammad F T S

28 мар. 2021 г.

this was hard but insightful

автор: Mark R

22 янв. 2019 г.

Good, short, overview of PCA

автор: Changxin W

28 янв. 2019 г.

Many errors of homework

автор: Poomphob S

18 июня 2020 г.

so challenging for me

автор: Sammy R

25 дек. 2019 г.

Needs more details

автор: Shreyas S S

20 июня 2020 г.

Good Course

автор: NITESH J

28 авг. 2020 г.

kinda long

автор: Raihan N J M

12 мар. 2021 г.


автор: Harrison B

18 апр. 2020 г.

Broadly speaking, this is a good course. However, the feeling is that it should be twice as long and with more videos. There is simply not enough instruction to facilitate clear learning and completion of this course is down to an individual's desire to read around and problem solve.

In particular, the programming assignments - whilst not technically difficult, lack clear articulation of expectation, which is compounded by pythons slightly inconvenient handling of matrices. Writing vectorised code which involves 1 x N or N x 1 matrices and transpositions often results in zero marks; with no clue whether the code is wrong, the student has misunderstood the expectation or python is refusing to recognise a N x 1 matrix. This could br helped by including more discriptions about the data sets and the variables being used, as well as the expectation of the output.

There are a lot of positives about this course, the videos are well made and are clear. Excellent supplementary learning if you're doing undergraduate Linear Algebra or other Machine Learning courses; just a bit too cramped for a standalone course (even with the others in the specialisation being well understood). Perhaps a four course could be added to this specialisation for "The Basics of Python for Machine Learning" where a student covers all the relevant coding knowledge?