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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

2,819 calificaciones
702 reseña

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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


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.

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451 - 475 de 700 revisiones para Mathematics for Machine Learning: PCA

por Xin W

6 de sep. de 2019

This course is full of mathematical derivation, so it is kind of boring.

por Bintang K P P

26 de mar. de 2021

We need more basic example and exercise before taking graded assignment

por Felipe T B

10 de ago. de 2020

Computational exercises could have more support from the professors.

por Jiaxuan L

15 de jul. de 2019

Overall a good course. Very limited introduction to Python though.

por Chow K M

28 de jul. de 2020

Quite challenging. Need to keep notes for programming assignment.

por Lafite

4 de feb. de 2019


por Attili S

19 de ago. de 2020

Great course! It could have elaborated more in the week 4 PCA

por Chenyu W

24 de jul. de 2021

feels like it progresses too fast. otherwise great content

por Ashok B B

6 de feb. de 2020

Course was challenging , but learned the maths behind PCA,

por Cesar A P C J

23 de dic. de 2018

Good content, just need to fix the assignments' platform.

por Dave D

30 de may. de 2020

This course was a fair overview of a very complex topic.


13 de may. de 2020

It is very informative and hands-on based Course for PCA

por Saiful B I

4 de may. de 2020

Not as good as the other two courses..but interesting!

por Sharon P

24 de sep. de 2018

Mathematically challenging, but satisfying in the end.

por Paulo Y C

11 de feb. de 2019

great material but explanation are a little bit messy

por Anas E j

19 de jun. de 2022

Thank you for this course , hope to learn more !

por Mohamed A M A

21 de feb. de 2021

Good course, but requires mathematical background

por taeha k

27 de jul. de 2019

Good but slightly less deeper than the other two

por Eddery L

24 de may. de 2019

The instructor is great. HW setup sucks though.

por Manish C

6 de may. de 2020

Best course for machine learning enthusiast

por Thijs S

28 de sep. de 2020

The last assignment could use improvement.

por I M A D W P M

27 de mar. de 2022

a really good course but also really hard

por J N B P

10 de sep. de 2020

Good for intermediates in linear algebra.

por Romesh M P

16 de ene. de 2020

Too much non-video lectures (lot to read)

por 3047 T

13 de jul. de 2020

The last course could have been better.