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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: 2,809
Рецензии: 698

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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201–225 из 695 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Lee F

28 сент. 2018 г.

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.

автор: Nileshkumar R P

6 мая 2020 г.

This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.

автор: Carlos J B A

17 мая 2021 г.

Undoubtedly one of the best courses I have taken on mathematics for Machine Learning with world-class teachers.

автор: Kuntal T

15 февр. 2021 г.

one of the best course to learn whats happening in machine learning and how it make sense through mathematics.

автор: 037 N S

30 июля 2020 г.

The PCA part Was a bit tricky barely handle the concepts.

thank you imperial team for such interactive course

автор: Krzysztof

21 авг. 2019 г.

One of the most challenging course in my life - almost impossible without python and mathematics background.

автор: Javier d V

25 июня 2021 г.

Great course. An intermediate mathematical background is requiered. This is a strength in terms of learning

автор: Pratama A A

25 авг. 2020 г.

Need more Effort to grasp the materials explained_-" you need to be patience,the lecturer is really on top

автор: Nelson S S

29 июля 2020 г.

Excellent course ... Quite challenging, a little difficult but I have learned a lot ... Thank you ...

автор: sameen n

6 сент. 2019 г.

Amazing course and provides basic introduction for the PCA. Need for programming help in this course.

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: Haoquan F

13 февр. 2022 г.

It's overall wonderful but the week 4's programming assignment really struggled and confused me.


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: Farhan F

26 мар. 2022 г.

T​his is very very very very very challengging, but i can do it because i try try and try

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!

автор: Mohammad A M

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