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

1,631 évaluations
367 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


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.


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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126 - 150 sur 368 Avis pour Mathematics for Machine Learning: PCA

par Benjamin C

Jan 28, 2020

Excellent course regarding both theoritical and practical sides.

par Shahriyar R

Sep 14, 2019

The hardest one but still useful, very informative neat concepts

par J G

May 12, 2018

This is a good course, you learn about the foundations of PCA.

par Harish S

Nov 24, 2019

This was a difficult course but still very informative.

par Oleg B

Jan 06, 2019

Excellent focus on important topics that lead up to PCA

par Lahiru D

Sep 16, 2019

Great course. Assignments are tough and challenging.

par Archana D

Mar 06, 2020

Brilliant work, references and formulas aided a lot

par Tichakunda

Jan 18, 2019

good course, rigorous proof and practical exercises

par Diego S

May 02, 2018

Difficult! But I did it :D And I learnt a lot...


Feb 03, 2020

A good representation after preceding courses.

par Wang S

Oct 21, 2019

A little bit difficult but helpful, thank you!

par Murugesan M

Jan 15, 2020

Excellent! very intuitive learning approach!!

par Hritik K S

Jun 20, 2019

Maths is just like knowing myself very well!

par K A K

May 22, 2020

Learnt many new things I didn't know before

par Naggita K

Dec 19, 2018

Great course. Rich well explained material.

par Jonathon K

Apr 13, 2020

Great course. Extremely smart lecturer.

par Xi C

Dec 31, 2018

Great course. Cover rigorous materials.

par Akshaya P K

Jan 25, 2019

This was a tough course. But worth it.


May 24, 2020

Thank you for offering a nice course.

par Eli C

Jul 22, 2018

very challenging and rewarding course

par 任杰文

May 13, 2019

It's great, interesting and helpful.

par Jyothula S K

May 18, 2020

Very Good Course to Learn about PCA

par Carlos S

Jun 11, 2018

What you need to understand PCA!!!

par Gautham T

Jun 16, 2019

excellent course by imperial

par Ankur A

May 15, 2020

Tough course, learnt a lot.