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Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

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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

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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451 - 475 de 706 revisiones para Mathematics for Machine Learning: PCA

por Max W

19 de abr. de 2020

Very challenging, could have used a few more videos to really explain or give a few more examples

por Abhishek T

12 de abr. de 2020

The structure could have been better. Some of the weeks were too crowded as compared to others

por Phuong A V

7 de ago. de 2020

very difficult course. But I hope that it will be useful fore my machine learning studying

por kerryliu

30 de jul. de 2018

still have room for improvement since lots of stuffs can be discussed more in detail.

por Ruan V S

13 de oct. de 2019

Harder than expected, the content is good and is well worth the struggle!

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

编程练习的质量不够高,不管是编程练习本身的代码逻辑、注释、练习的质量还是在答疑区课程组的答疑都不能尽如人意,对于编程练习并不很满意

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.

por ADITYA K

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