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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: 2,695
Рецензии: 675

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:

426–450 из 671 отзывов о курсе Mathematics for Machine Learning: PCA

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

автор: Abhishek T

12 апр. 2020 г.

The structure could have been better. Some of the weeks were too crowded as compared to others

автор: Phuong A V

7 авг. 2020 г.

very difficult course. But I hope that it will be useful fore my machine learning studying

автор: kerryliu

30 июля 2018 г.

still have room for improvement since lots of stuffs can be discussed more in detail.

автор: Ruan V S

13 окт. 2019 г.

Harder than expected, the content is good and is well worth the struggle!

автор: Xin W

6 сент. 2019 г.

This course is full of mathematical derivation, so it is kind of boring.

автор: Bintang K P P

26 мар. 2021 г.

We need more basic example and exercise before taking graded assignment

автор: Felipe T B

10 авг. 2020 г.

Computational exercises could have more support from the professors.

автор: Jiaxuan L

15 июля 2019 г.

Overall a good course. Very limited introduction to Python though.

автор: Chow K M

28 июля 2020 г.

Quite challenging. Need to keep notes for programming assignment.

автор: Lafite

4 февр. 2019 г.


автор: Attili S

19 авг. 2020 г.

Great course! It could have elaborated more in the week 4 PCA

автор: Chenyu W

24 июля 2021 г.

feels like it progresses too fast. otherwise great content

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.