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: 2,605
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

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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## 201–225 из 644 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Brian H

24 февр. 2020 г.

Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.

автор: Natalya T

25 февр. 2019 г.

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

автор: Aishik R

18 янв. 2020 г.

Excellent and to-the-point explanations, useful assignments to make the concepts etched in memory

автор: KAMASANI V R

20 июня 2020 г.

This course helped me in getting a deeper knowledge on Principal Component Analysis. Thank You.

автор: Wei X

16 окт. 2018 г.

concise and to the point. Might want to introduce a bit the technique of Lagrangin multiplier

автор: Leonardo H T S

2 мая 2021 г.

This was an amazing course, I really enjoyed it and learn a lot!

Thank you so much, greetings

автор: Wahyu N A M

27 мар. 2021 г.

I'm struggle with assigments of week 4 about implementing PCA. But, yeaah finally i got this

автор: Mayank

3 дек. 2020 г.

This course cleared so many concepts and enabled me to further master the subject on my own.

автор: Ripple S

17 мар. 2020 г.

I learnt a lot from this course and now I think I am much more familiar with this algorithm.

автор: Haofei M

22 апр. 2020 г.

extremely informative and really help me understand the basic math in Machine learning

автор: Deepak T

17 апр. 2020 г.

Course was challenging, so does the math. It was a very excellent learning experience!

14 нояб. 2019 г.

This course is also so helpful, and the lecturer is so predominant on what he taught.

автор: Alfonso J

20 окт. 2019 г.

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

автор: MD K A

8 авг. 2020 г.

Algebra, Calculus and PCA

These are all excellent, if you have mathematics knowledge

автор: Arijit B

5 нояб. 2019 г.

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

автор: Pascal U E

25 мая 2018 г.

Very hard to follow, but you need to do it to understand machine learning very well.

автор: Greg E

27 июля 2019 г.

I have thoroughly enjoyed every course of this specialization. Thank you very much.

автор: Faruk Y

22 сент. 2019 г.

Lectures and programming assignments were selected nicely to teach the math of PCA

автор: Sanjay B

30 дек. 2020 г.

Excellent program, helped get to understand features of Python programming fast

автор: Lia L

22 мая 2019 г.

This was really difficoult, but I'm so proud for the completion of the course.

автор: Pritam C

22 сент. 2020 г.

It was an intense Math Class with a piece of new knowledge about PCA...Thanks

автор: Roshan C

23 нояб. 2019 г.

the course was very much intuitive and helpful to grasp the knowledge of PCA

автор: Hanif Y A P M

1 мар. 2021 г.

I think there must be correction for the pca lab, the testing code is error

автор: Pramod H K

7 авг. 2020 г.

The highly mathematical perspective of PCA with greater conceptualization.

автор: Rishabh A

17 июня 2019 г.

We need more elaborate explanation at few tricky places during the course.