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Вернуться к Mathematics for Machine Learning: PCA

Отзывы учащихся о курсе Mathematics for Machine Learning: PCA от партнера Имперский колледж Лондона

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Оценки: 1,274
Рецензии: 272

О курсе

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....

Лучшие рецензии

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!

Фильтр по:

101–125 из 269 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Hritik K S

Jun 20, 2019

Maths is just like knowing myself very well!

автор: Naggita K

Dec 19, 2018

Great course. Rich well explained material.

автор: Xi C

Dec 31, 2018

Great course. Cover rigorous materials.

автор: Akshaya P K

Jan 25, 2019

This was a tough course. But worth it.

автор: Eli C

Jul 22, 2018

very challenging and rewarding course

автор: 任杰文

May 13, 2019

It's great, interesting and helpful.

автор: Carlos S

Jun 11, 2018

What you need to understand PCA!!!

автор: Gautham T

Jun 16, 2019

excellent course by imperial

автор: imran s

Dec 20, 2018

Great Coverage of the Topic

автор: Ajay S

Apr 09, 2019

Great course for every one

автор: Ricardo C V

Dec 25, 2019

Challenging but Excellent

автор: Keisuke F

Sep 15, 2019

I had big fun of PCA

автор: Sujeet B

Jul 21, 2019

Tough, but great!

автор: Jitender S V

Jul 25, 2018

AWESOME!!!!!!!!!!

автор: Shanxue J

May 23, 2018

Truly exceptional

автор: Lintao D

Sep 24, 2019

Very Good Course

автор: Shounak D

Sep 15, 2018

Great course !

автор: Andrey

Sep 17, 2018

Great course!

автор: Samresh

Aug 10, 2019

Nice Course.

автор: David N

Jul 24, 2019

Great course

автор: Mohamed H

Aug 10, 2019

fantastic

автор: Karthik

May 03, 2018

RRhis cl

автор: Akash G

Mar 20, 2019

awesome

автор: Bálint - H F

Mar 20, 2019

Great !

автор: HARSH K D

Jun 28, 2018

good