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Avis et commentaires pour d'étudiants pour Mathematics for Machine Learning: PCA par Imperial College London

4.0
étoiles
1,621 évaluations
362 avis

À propos du cours

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....

Meilleurs avis

JS

Jul 17, 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.

JV

May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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201 - 225 sur 362 Avis pour Mathematics for Machine Learning: PCA

par João M G

Aug 14, 2019

The course was great till the final week. The lectures did not explain very well the concepts and the assignment was poorly designed. It's a shame because I've loved the more rigorous way of this final course.

par ranzhang

Aug 29, 2019

I think it's really a hard lesson for me, but I've also learn a lot, thanks a lot for the teacher and coursera. Some Programming test take too long to execute, and there are some errors in it. just be careful

par Suyog P

Sep 02, 2019

Finally understood basic intuition of PCA, never got perfect resource before. However, there was a sharp change in terms of course delivery than the previous two courses of this specialization. So, heads up.

par Camilo J

Mar 01, 2019

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

par Abhishek P

Sep 09, 2019

Course content tackles a difficult topic well. Only flaw is that programming assignments are poorly designed in some places and are quite difficult to pick up at times.

par Liang S

Jul 09, 2018

Teaching pacing is good, and clear in explanation. It will be good if there are some examples about how we should apply all these theories to some real problems.

par Mohamed B

Oct 27, 2019

I learned a lot in this course, though the last week was somehow hurried and the lecturer didn't spend enough time to piece the whole stuff together.

par Rok Z

Feb 05, 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.

par Jordan V

Aug 23, 2019

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

par Kevin G

Jan 14, 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.

par Helena S

Feb 28, 2020

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

par Giri G

Jun 07, 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

par Christine W

Aug 13, 2018

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

par Shaiman Z S

Apr 30, 2020

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

par Hilmi E

Apr 20, 2020

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

par Voravich C

Oct 21, 2019

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

par Nguyen D P

Oct 17, 2018

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

par Ananthesh J S

Jun 16, 2018

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

par Manuel I

Jul 07, 2018

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

par Shraavan S

Mar 04, 2019

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

par Andrew D

Jun 02, 2019

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

par Ibon U E

Jan 07, 2020

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

par Max W

Apr 20, 2020

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

par Abhishek T

Apr 12, 2020

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

par kerryliu

Jul 30, 2018

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