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學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

4.0
1,386 個評分
305 條評論

課程概述

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....

熱門審閱

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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101 - Mathematics for Machine Learning: PCA 的 125 個評論(共 304 個)

創建者 Lia L

May 22, 2019

This was really difficoult, but I'm so proud for the completion of the course.

創建者 Roshan C

Nov 23, 2019

the course was very much intuitive and helpful to grasp the knowledge of PCA

創建者 Rishabh A

Jun 17, 2019

We need more elaborate explanation at few tricky places during the course.

創建者 Benjamin C

Jan 28, 2020

Excellent course regarding both theoritical and practical sides.

創建者 Shahriyar R

Sep 14, 2019

The hardest one but still useful, very informative neat concepts

創建者 J G

May 12, 2018

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

創建者 Harish S

Nov 24, 2019

This was a difficult course but still very informative.

創建者 Oleg B

Jan 06, 2019

Excellent focus on important topics that lead up to PCA

創建者 Lahiru D

Sep 16, 2019

Great course. Assignments are tough and challenging.

創建者 Archana D

Mar 06, 2020

Brilliant work, references and formulas aided a lot

創建者 Tichakunda

Jan 18, 2019

good course, rigorous proof and practical exercises

創建者 Diego S

May 02, 2018

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

創建者 CHIOU Y C

Feb 03, 2020

A good representation after preceding courses.

創建者 Wang S

Oct 21, 2019

A little bit difficult but helpful, thank you!

創建者 Murugesan M

Jan 15, 2020

Excellent! very intuitive learning approach!!

創建者 Hritik K S

Jun 20, 2019

Maths is just like knowing myself very well!

創建者 Naggita K

Dec 19, 2018

Great course. Rich well explained material.

創建者 Xi C

Dec 31, 2018

Great course. Cover rigorous materials.

創建者 Akshaya P K

Jan 25, 2019

This was a tough course. But worth it.

創建者 Eli C

Jul 22, 2018

very challenging and rewarding course

創建者 任杰文

May 13, 2019

It's great, interesting and helpful.

創建者 Carlos S

Jun 11, 2018

What you need to understand PCA!!!

創建者 Gautham T

Jun 16, 2019

excellent course by imperial

創建者 imran s

Dec 20, 2018

Great Coverage of the Topic

創建者 Ajay S

Apr 09, 2019

Great course for every one