Вернуться к Mathematics for Machine Learning: PCA

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

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Оценки: 2,607
Рецензии: 648

## 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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## 226–250 из 645 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Aman M

1 июля 2020 г.

good content but assignment quality and maintenance should be rechecked

автор: Seelam S

25 июля 2020 г.

Good Course to get knowledge of Maths required for Machine Learning! ☺

автор: Sanchayan D

7 июня 2020 г.

Good Introduction to understanding the principal component analysis

автор: Benjamin C

28 янв. 2020 г.

Excellent course regarding both theoritical and practical sides.

автор: Shahriyar R

14 сент. 2019 г.

The hardest one but still useful, very informative neat concepts

автор: J G

12 мая 2018 г.

This is a good course, you learn about the foundations of PCA.

автор: Opas S

15 июля 2020 г.

Great course for improve math skilled and improve basic to ML

автор: Isaac M M

9 авг. 2020 г.

A bit more difficult than previous ones but it is worth it

автор: Phani B R P

1 июня 2020 г.

Very good course and extremely challenging, especially PCA

автор: Anh V

15 нояб. 2020 г.

Very detailed explanation and mathematics underlying PCA!

автор: Md A A M

24 авг. 2020 г.

Great Course. Everyone should take this course. Thanks.

автор: Harish S

24 нояб. 2019 г.

This was a difficult course but still very informative.

автор: Oleg B

6 янв. 2019 г.

Excellent focus on important topics that lead up to PCA

автор: Kaustubh S

29 нояб. 2020 г.

Very tough course but got a good sense of what PCA is

автор: Prateek S

14 июля 2020 г.

best course and important to study with concentration

автор: Lahiru D

16 сент. 2019 г.

Great course. Assignments are tough and challenging.

автор: Archana D

6 мар. 2020 г.

Brilliant work, references and formulas aided a lot

автор: Tichakunda

18 янв. 2019 г.

good course, rigorous proof and practical exercises

автор: Diego S

2 мая 2018 г.

Difficult! But I did it :D And I learnt a lot...

автор: CHIOU Y C

3 февр. 2020 г.

A good representation after preceding courses.

автор: Wang S

21 окт. 2019 г.

A little bit difficult but helpful, thank you!

автор: eder p g

9 авг. 2020 г.

excellent!!!! it's very useful and practical.

автор: Murugesan M

15 янв. 2020 г.

Excellent! very intuitive learning approach!!

автор: Hritik K S

20 июня 2019 г.

Maths is just like knowing myself very well!

автор: K A K

22 мая 2020 г.

Learnt many new things I didn't know before