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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: 2,685
Рецензии: 673

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.

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251–275 из 669 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Goh K L

8 авг. 2021 г.

Decently challenging and therefore very fruitful.

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

автор: Naggita K

19 дек. 2018 г.

Great course. Rich well explained material.

автор: Sivasankar S

3 авг. 2021 г.

This course is very informative and useful

автор: Carlos E G G

28 сент. 2020 г.

Really difficult, but worth it in the end.

автор: Zongrui H

11 мая 2021 г.

PCA assignment in week4 is a chanllenge!

автор: Binu V P

8 июня 2020 г.

best course I had ever done in coursera

автор: Jonathon K

13 апр. 2020 г.

Great course. Extremely smart lecturer.

автор: Xi C

31 дек. 2018 г.

Great course. Cover rigorous materials.

автор: Akshay K

25 янв. 2019 г.

This was a tough course. But worth it.

автор: Wassana K

22 мар. 2021 г.

Programming Assignment is so hard !!!


24 мая 2020 г.

Thank you for offering a nice course.

автор: Eli C

21 июля 2018 г.

very challenging and rewarding course

автор: Indria A

26 мар. 2021 г.

very very tiring but fun, thank you.

автор: Jeff D

1 нояб. 2020 г.

Thank you very much for this course.

автор: 任杰文

13 мая 2019 г.

It's great, interesting and helpful.

автор: Jyothula S K

18 мая 2020 г.

Very Good Course to Learn about PCA

автор: Carlos S

11 июня 2018 г.

What you need to understand PCA!!!

автор: 祈璃

9 июля 2021 г.

This module is quite challenging!