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

4.0
étoiles
1,642 évaluations
371 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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151 - 175 sur 373 Avis pour Mathematics for Machine Learning: PCA

par Ankur A

May 15, 2020

Tough course, learnt a lot.

par imran s

Dec 20, 2018

Great Coverage of the Topic

par Ajay S

Apr 09, 2019

Great course for every one

par Ricardo C V

Dec 25, 2019

Challenging but Excellent

par Yasser Z S E

May 26, 2020

Thank you very match

par wonseok k

Mar 03, 2020

hard but good course

par Keisuke F

Sep 15, 2019

I had big fun of PCA

par N'guessan L R G

Apr 15, 2020

Amazing Course!!!!

par Dominik B

Feb 17, 2020

Great instructor!

par Sujeet B

Jul 21, 2019

Tough, but great!

par Jitender S V

Jul 25, 2018

AWESOME!!!!!!!!!!

par Shanxue J

May 23, 2018

Truly exceptional

par Lintao D

Sep 24, 2019

Very Good Course

par Shounak D

Sep 15, 2018

Great course !

par Andrey

Sep 17, 2018

Great course!

par Samresh

Aug 10, 2019

Nice Course.

par David N

Jul 24, 2019

Great course

par Salah T

Apr 26, 2020

Many thanks

par Artur

Feb 29, 2020

good course

par Mohamed H

Aug 10, 2019

fantastic

par Karthik

May 03, 2018

RRhis cl

par Akash G

Mar 20, 2019

awesome

par Bálint - H F

Mar 20, 2019

Great !

par HARSH K D

Jun 28, 2018

good

par Niju M N

Apr 09, 2020

This is the final course in the Specialization, that focuses on Principal component Analysis.This course is a bit hard compared to the other two courses in specialization. This builds on the topics explained in the other two courses.The Instructor tries to squeeze the concepts in the limited time.Not all materials are completely explained in the video, however, students can refer to other materials available in the web/ Refer the course forums and get the concepts and use them to solve the Quizzes. Some times the Assignments and quizzes are frustrating , however they do a good job of reinforcing the ideas taught in the video. Totally this is a good time spent .