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Volver a Mathematics for Machine Learning: PCA

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

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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....

Principales reseñas

WS

6 de jul. de 2021

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.

JS

16 de jul. de 2018

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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301 - 325 de 706 revisiones para Mathematics for Machine Learning: PCA

por Gautham T

16 de jun. de 2019

excellent course by imperial

por Ankur A

15 de may. de 2020

Tough course, learnt a lot.

por Imran S

19 de dic. de 2018

Great Coverage of the Topic

por Ajay S

20 de feb. de 2021

Great course for every one

por Felix G S S

27 de mar. de 2021

Wow, it is so challenging

por Ricardo C V

25 de dic. de 2019

Challenging but Excellent

por CHAITANYA V

17 de jul. de 2020

Excellent course content

por Mayank K

2 de jul. de 2020

This course is very good

por Nihal T

13 de jul. de 2022

Amazing Specialization!

por Michael

3 de ago. de 2021

I strongly recommend it

por Subhodip P

15 de dic. de 2020

Awesome course loved it

por Pranav N

25 de ago. de 2020

Amazing overall course

por iorilu

3 de jun. de 2021

intuitive and helpful

por Gazi J H

16 de oct. de 2020

Thank you very much.

por Yasser Z S E

26 de may. de 2020

Thank you very match

por wonseok k

3 de mar. de 2020

hard but good course

por 福永圭佑

15 de sep. de 2019

I had big fun of PCA

por Rajkumar R

20 de jun. de 2020

I enjoyed learning.

por Jason K

24 de jul. de 2021

Excellent Course !

por Omar Y B L

15 de jul. de 2020

Cruel pero justo!!

por N'guessan L R G

14 de abr. de 2020

Amazing Course!!!!

por Dominik B

17 de feb. de 2020

Great instructor!

por Sujeet B

21 de jul. de 2019

Tough, but great!

por Jitender S V

25 de jul. de 2018

AWESOME!!!!!!!!!!

por Shanxue J

23 de may. de 2018

Truly exceptional