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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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426 - 450 de 700 revisiones para Mathematics for Machine Learning: PCA

por Rok Z

5 de feb. de 2020

A different course than the previous 2.

Much harder - as you have to actually know some Python tricks.

But I guess it's the same in a real world.

por Jordan V

23 de ago. de 2019

Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.

por Kevin G

14 de ene. de 2020

Felt like explanations in this course were a bit confusing, but otherwise, it was a very interesting course. Thank you so much for doing this.

por Helena S

28 de feb. de 2020

The final Notebook contains some errors (Xbar instead of X, passed as an argument). Otherwise a very well organized course. Thanks a lot!

por Giri G

7 de jun. de 2019

This was a very hard course for me. But I think the instructor has done the best possible he can with presenting and explaining the course

por Leon T

10 de jul. de 2020

Jupyter notebook assignments are in desperate need of attention! Very buggy or non-intuitive for the scope of material in span of time.

por Christine W

13 de ago. de 2018

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

por Hưng P

9 de mar. de 2022

The lecture & instructor are great. But the grader in the assignments really needs revisions. It caused lots of unnecessary stress

por Shaiman S

30 de abr. de 2020

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

por Karan S

1 de ago. de 2020

Focus a bit more on PCA in week 4, week 1 was not very informative and should be assumed as required knowledge for the course

por Hilmi E

20 de abr. de 2020

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

por Voravich C

21 de oct. de 2019

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

por Phuong N

17 de oct. de 2018

That's a great online courses can help people have enough background to break into Machine Learning or Data science

por Ananthesh J S

16 de jun. de 2018

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

por Manuel I

7 de jul. de 2018

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

por Shraavan S

4 de mar. de 2019

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

por Andrew D

2 de jun. de 2019

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

por Neelam U

23 de sep. de 2020

The programming assignments were quite challenging. Some part of the course can discuss this aspect as well.

por Paulo N A J

18 de ago. de 2020

It is a good course with hard programming, but the assignments could be improved. The forum helps a lot.

por Ibon U E

7 de ene. de 2020

The derivations of some concepts have been more vague compared to other courses in this specialization.

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!