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学生对 Imperial College London 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

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3,155 个评分

课程概述

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....

热门审阅

JS

Jul 16, 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.

WS

Jul 6, 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

筛选依据：

301 - Mathematics for Machine Learning: PCA 的 325 个评论（共 791 个）

创建者 K L G

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Aug 8, 2021

Decently challenging and therefore very fruitful.

创建者 Diego S

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May 2, 2018

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

创建者 Ida B R A M M

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Mar 27, 2022

Very HARD but fundamentals are important, yes?

创建者 Levi C

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Feb 3, 2020

A good representation after preceding courses.

创建者 Wang S

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Oct 20, 2019

A little bit difficult but helpful, thank you!

创建者 eder p g

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Aug 9, 2020

excellent!!!! it's very useful and practical.

创建者 Mohd. F I S

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Jun 7, 2022

Good. But Programming exercise is not clear

创建者 Hritik K S

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Jun 20, 2019

Maths is just like knowing myself very well!

创建者 K A K

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May 21, 2020

Learnt many new things I didn't know before

创建者 Naggita K

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Dec 19, 2018

Great course. Rich well explained material.

创建者 Sivasankar S

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Aug 3, 2021

This course is very informative and useful

创建者 Carlos E G G

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Sep 27, 2020

Really difficult, but worth it in the end.

创建者 Zongrui H

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May 11, 2021

PCA assignment in week4 is a chanllenge!

创建者 venky i

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Sep 10, 2025

Great Learning and Thanks to lecturers

创建者 Binu V P

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Jun 8, 2020

best course I had ever done in coursera

创建者 Jonathon K

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Apr 12, 2020

Great course. Extremely smart lecturer.

创建者 Ronnie C

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Dec 31, 2018

Great course. Cover rigorous materials.

创建者 Juan B J

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Apr 3, 2023

Very interesting, thank you very much.

创建者 Akshaya P K

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Jan 24, 2019

This was a tough course. But worth it.

创建者 Carlos A V P

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Jan 15, 2022

Excelente curso, muy claro y retador

创建者 Wassana K

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Mar 21, 2021

Programming Assignment is so hard !!!

创建者 THIRUPATHI T

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May 24, 2020

Thank you for offering a nice course.

创建者 Eli C

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Jul 21, 2018

very challenging and rewarding course

创建者 Indria A

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Mar 26, 2021

very very tiring but fun, thank you.