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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: 2,697
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:

651–671 из 671 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Sairam K

9 янв. 2021 г.

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

автор: Daniel C

20 авг. 2021 г.

​the lecture videos do not seem to provide enough guidance for the assignments

автор: TUSHAR K

19 июля 2020 г.

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

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: Wensheng Z

24 нояб. 2019 г.

Jumpy instruction with little illustrations

автор: Adam C

31 окт. 2019 г.

Worst course I've ever taken, online or IRL

автор: Zecheng W

19 окт. 2019 г.

Poorly organized and extremely confusing

автор: Mingzhe D

11 дек. 2019 г.

Assignment 1 cannot be passed!

автор: Cintya K M

2 мар. 2021 г.

confuse , difficuld and weird

автор: 朱嘉懿

25 июня 2020 г.

The assignment worked badly.

автор: Syed s A

23 июля 2020 г.

Assignment is not proper

автор: Анофриев А

1 окт. 2019 г.

The worst course ever

автор: Bohdan S

17 февр. 2020 г.

Worst course ever

автор: Ankit M

12 июля 2020 г.


автор: Arjunsiva S

4 окт. 2020 г.