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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## Acerca del Curso

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 youâ€™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 reseÃ±as

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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## 251 - 275 de 719 revisiones para Mathematics for Machine Learning: PCA

por Sanchayan D

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7 de jun. de 2020

Good Introduction to understanding the principal component analysis

por Sekhar K

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18 de ago. de 2021

Excellent course! Really enjoyed it. All professors were great!!

por Benjamin C

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28 de ene. de 2020

Excellent course regarding both theoritical and practical sides.

por Shahriyar R

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14 de sep. de 2019

The hardest one but still useful, very informative neat concepts

por J G

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12 de may. de 2018

This is a good course, you learn about the foundations of PCA.

por Opas S

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15 de jul. de 2020

Great course for improve math skilled and improve basic to ML

por Puja P N B M

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29 de mar. de 2022

PCA assigment i dont have ideas but overall course is good

por Isaac M M

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9 de ago. de 2020

A bit more difficult than previous ones but it is worth it

por Phani B R P

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1 de jun. de 2020

Very good course and extremely challenging, especially PCA

por Anh V

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15 de nov. de 2020

Very detailed explanation and mathematics underlying PCA!

por Md. A A M

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24 de ago. de 2020

Great Course. Everyone should take this course. Thanks.

por Harish S

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24 de nov. de 2019

This was a difficult course but still very informative.

por Oleg B

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6 de ene. de 2019

Excellent focus on important topics that lead up to PCA

por Kaustubh S

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29 de nov. de 2020

Very tough course but got a good sense of what PCA is

por Prateek S

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14 de jul. de 2020

best course and important to study with concentration

por Lahiru D

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16 de sep. de 2019

Great course. Assignments are tough and challenging.

por Archana D

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6 de mar. de 2020

Brilliant work, references and formulas aided a lot

por Tich M

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18 de ene. de 2019

good course, rigorous proof and practical exercises

por Goh K L

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8 de ago. de 2021

Decently challenging and therefore very fruitful.

por Diego S

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2 de may. de 2018

Difficult! But I did it :D And I learnt a lot...

por Ida B R A M M

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27 de mar. de 2022

Very HARD but fundamentals are important, yes?

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3 de feb. de 2020

A good representation after preceding courses.

por Wang S

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21 de oct. de 2019

A little bit difficult but helpful, thank you!

por eder p g

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9 de ago. de 2020

excellent!!!! it's very useful and practical.

por Murugesan M

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15 de ene. de 2020

Excellent! very intuitive learning approach!!