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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

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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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626 - 650 de 706 revisiones para Mathematics for Machine Learning: PCA

por Bohdan K

13 de ago. de 2020

The course is awful, it's nothing compare to previous 2 courses. It has a lot of errors in assignments objectives and quizzes! The explanation is complete crap! I'm wondering how was it even allowed on Coursera?!

por Ivo R

16 de nov. de 2019

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

por raghu c b

4 de abr. de 2020

Needs to demo a little bit of code owing to the complexity of the course content.Lectures gives just a high level understanding only. Assignments are taking far more complicated than expected.

por Paulo H S G

27 de nov. de 2020

Even though the videos and quizzes are well produced and informative, the assignments are so poorly designed that they can only bring about some frustration with the learning process.

por Yi S

11 de jun. de 2021

assignment and quiz are not well designed. the knowledge covered in lectures are not enough to complete assignments. The first two courses in this specialization is much better.

por Nidhi G

23 de ago. de 2020

Faced a lot of problems in exercises. Don't feel that i have completely understood the concepts. This course can be made more learner friendly with better explanations.

por vignesh n

12 de sep. de 2018

Explaination of many things are skipped, assumption was made by the instructor that lot of things were already known by the learner. It could have been much better.

por Maksim S

25 de mar. de 2020

The difficulty of the course is inadequate and the pace is not balanced. Requires a lot of search for additional resources to understand materials. I cancelled.

por Ghanem A

20 de jul. de 2021

Response to questions is very slow. Support to learners is not sufficient

Programming assignments are not explained well (some I believe have errors)

por Kovendhan V

11 de jul. de 2020

After first two amazing courses in this specialisation, third course was a huge let down. One skill I learnt from this last course is patience.

por Martin H

8 de dic. de 2019

Lack of examples to clarify abstract concepts. Big contrast in quality compared to the other courses in this specialization.

por Jamiul H D

7 de ago. de 2020

Poor explanation by the instructor. Previous ones were very helpful. I didn't understand many topics well

por Lavanith T

21 de ago. de 2020

Everything is okay but there is a huge drawback with the programming explanation part.

por Xiao L

3 de jun. de 2019

very wired assignment, a lot of error in template code. The concept is not clear.

por Sai M B

3 de ago. de 2020

The lectures were not clear. I had to use other sources to understand lectures.

por Pawan K S

20 de jun. de 2020

This course was the hardest I encountered in this specialisation.

por Mohamed A H

18 de ago. de 2021

it was not clear alot of the time and it was really hard

por Kirill T

26 de jul. de 2020

Way worse than the previous courses. Lacks explanations

por Kevin O

27 de mar. de 2021

Really interesting topic but not nearly enough detail.

por Amr F M R

22 de sep. de 2020

I think course material was not explained well at all.

por Timothy M

22 de abr. de 2021

The lectures and assignments did not synergize well.

por Aravindan B

23 de sep. de 2019

Need to improve the content and delivery of content.

por Mohammed A A

19 de jul. de 2020

the course is too shallow with difficult code exame

por Scoodood C

28 de jul. de 2018

Video lecture not as intuitive as previous courses.

por Michael B

21 de nov. de 2019

Programming assignments not well explained