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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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326 - 350 de 706 revisiones para Mathematics for Machine Learning: PCA

por Sabrina M U

27 de mar. de 2021

alhamdulillah :)

por Habib B K

13 de mar. de 2021

Nice Chalengging

por Lintao D

24 de sep. de 2019

Very Good Course

por Divyansh K

30 de nov. de 2020

It was so tough

por Firli A R

27 de mar. de 2022

amazing course

por EDWARD J R

29 de nov. de 2020

Amazing course

por Shounak D

15 de sep. de 2018

Great course !

por Andrey

17 de sep. de 2018

Great course!

por Samresh

10 de ago. de 2019

Nice Course.

por David N

24 de jul. de 2019

Great course

por Snehal P

11 de sep. de 2020

Nice Course

por Manikant R

8 de jun. de 2020

Best course

por Salah T

26 de abr. de 2020

Many thanks

por Artur

29 de feb. de 2020

good course

por Bintang F E

28 de mar. de 2021

awesome!!

por Muhammad T R T P

28 de mar. de 2021

good one!

por Andreanov R

15 de mar. de 2021

very hard

por miguel s

20 de sep. de 2020

very well

por Mohamed H

10 de ago. de 2019

fantastic

por Karthik

3 de may. de 2018

RRhis cl

por Levina A

28 de mar. de 2021

So cool

por Al F N P M

12 de mar. de 2021

Finally

por Akash G

20 de mar. de 2019

awesome

por Bálint - H F

20 de mar. de 2019

Great !

por Sean F

22 de jun. de 2021

Tough.