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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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676 - 695 de 695 revisiones para Mathematics for Machine Learning: PCA

por Daniel C

20 de ago. de 2021

​the lecture videos do not seem to provide enough guidance for the assignments

por TUSHAR K

19 de jul. de 2020

Previous Two Courses were better in terms of both assignments and teaching.

por Siddharth S

4 de jun. de 2020

Very Poor when compared to previous two courses of this specialization.

por Saeif A

1 de ene. de 2020

This course was a disaster for me. The first two were great though.

por Jared E

25 de ago. de 2018

Impossible to do without apparently an indepth knowledge of python.

por Soumitri C

15 de dic. de 2020

okayish teaching but grading system is absolute rubbish in Week4

por Aditya P

4 de jul. de 2020

Very poor teaching and overall it's the worst course I've taken

por Ahmad O

27 de ago. de 2020

Very bad explanation. The assignments need more instructions.

por Aurel N

5 de jul. de 2020

k-NN assignment is full of errors and no proper explanations.

por Wensheng Z

24 de nov. de 2019

Jumpy instruction with little illustrations

por Adam C

31 de oct. de 2019

Worst course I've ever taken, online or IRL

por Zecheng W

19 de oct. de 2019

Poorly organized and extremely confusing

por Mingzhe D

11 de dic. de 2019

Assignment 1 cannot be passed!

por ML-07 C k

2 de mar. de 2021

confuse , difficuld and weird

por 朱嘉懿

25 de jun. de 2020

The assignment worked badly.

por Syed s A

23 de jul. de 2020

Assignment is not proper

por Анофриев А

1 de oct. de 2019

The worst course ever

por Bohdan S

17 de feb. de 2020

Worst course ever

por Ankit M

12 de jul. de 2020

POOR VERY POOR

por Arjunsiva S

4 de oct. de 2020

meh!