Chevron Left
Voltar para Mathematics for Machine Learning: PCA

Comentários e feedback de alunos de Mathematics for Machine Learning: PCA da instituição Imperial College London

2,867 classificações

Sobre o 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 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....

Melhores avaliações


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.


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:

1 — 25 de 712 Avaliações para o Mathematics for Machine Learning: PCA

por Hashaam S

30 de dez de 2018

por Maximilian W

29 de abr de 2019

por Eric P

26 de abr de 2019

por Vyacheslav T

24 de mar de 2019

por Christos M

27 de abr de 2019

por Avirup G

18 de fev de 2019

por Alexandra S

26 de set de 2018

por Bryan S

19 de fev de 2019

por Sreekar P

23 de out de 2018

por Harshit D

30 de jul de 2018

por Brock I

21 de nov de 2018

por Guillermo A

15 de jun de 2020

por Rahul M

29 de jun de 2019

por Roy A

23 de set de 2020

por Nimesh S

19 de jun de 2020

por João S

2 de mai de 2019

por Martin B

22 de out de 2018

por Jong H S

17 de jul de 2018

por Oliverio J S J

29 de mai de 2020

por Christian R

24 de jul de 2018


27 de out de 2018

por Jayant V

1 de mai de 2018

por José D

31 de out de 2018

por Thomas B

4 de jun de 2022

por A h b

21 de out de 2019