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

4.0
estrellas
2,866 calificaciones

## 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.

Filtrar por:

## 351 - 375 de 711 revisiones para Mathematics for Machine Learning: PCA

por BÃ¡lint - H F

â€¢

20 de mar. de 2019

por Sean F

â€¢

22 de jun. de 2021

â€¢

27 de mar. de 2022

por Insyiraah O A

â€¢

26 de mar. de 2022

por Mellania P S

â€¢

23 de mar. de 2021

por Indah D S

â€¢

9 de mar. de 2021

por Md. R Q S

â€¢

21 de ago. de 2020

por Faisal A M

â€¢

10 de abr. de 2022

por Doni S

â€¢

27 de mar. de 2022

por Suci A S

â€¢

20 de jun. de 2021

por Agung W

â€¢

28 de mar. de 2021

â€¢

20 de mar. de 2021

por GEETHA P

â€¢

28 de jul. de 2020

por RAGHUVEER S D

â€¢

25 de jul. de 2020

por Harsh D

â€¢

28 de jun. de 2018

por Amini D P S

â€¢

27 de mar. de 2022

â€¢

25 de mar. de 2021

por Roberto

â€¢

26 de mar. de 2021

por ahmed b

â€¢

15 de abr. de 2021

por Sherlock H

â€¢

23 de ago. de 2020

por ErtuÄŸrul G

â€¢

7 de jun. de 2020

por Niju M N

â€¢

9 de abr. de 2020

por John C B

â€¢

19 de jul. de 2021

por Vassiliy T

â€¢

10 de jul. de 2018

por Matteo L

â€¢

20 de abr. de 2020