Chevron Left
Retour à Mathematics for Machine Learning: PCA

Avis et commentaires pour d'étudiants pour Mathematics for Machine Learning: PCA par Imperial College London

2,841 évaluations

À propos du cours

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....

Meilleurs avis


6 juil. 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.


16 juil. 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.

Filtrer par :

1 - 25 sur 707 Avis pour Mathematics for Machine Learning: PCA

par Hashaam S

30 déc. 2018

par Maximilian W

29 avr. 2019

par Eric P

26 avr. 2019

par Christos M

27 avr. 2019

par Ткаченко В Е

24 mars 2019

par Avirup G

18 févr. 2019

par Alexandra S

26 sept. 2018

par Bryan S

19 févr. 2019

par Sreekar P

23 oct. 2018

par Harshit D

30 juil. 2018

par Brock I

21 nov. 2018

par Guillermo A

15 juin 2020

par Rahul M

29 juin 2019

par Roy A

23 sept. 2020

par Nimesh S

19 juin 2020

par João S

2 mai 2019

par Jong H S

17 juil. 2018

par Martin B

22 oct. 2018

par Oliverio J S J

29 mai 2020

par Christian R

24 juil. 2018


27 oct. 2018

par Jayant V

1 mai 2018

par José D

31 oct. 2018


21 oct. 2019

par Tobias L

10 sept. 2020