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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：

326 - Mathematics for Machine Learning: PCA 的 350 个评论（共 791 个）

创建者 Jeff D

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Nov 1, 2020

Thank you very much for this course.

创建者 任杰文

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May 13, 2019

It's great, interesting and helpful.

创建者 Jyothula S K

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May 18, 2020

Very Good Course to Learn about PCA

创建者 Thierry P

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Apr 21, 2022

good understanding of pca insight

创建者 Carlos S

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Jun 11, 2018

What you need to understand PCA!!!

创建者 祈璃

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Jul 8, 2021

This module is quite challenging!

创建者 Dina B

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Aug 8, 2020

Nice course - informative and fun

创建者 saketh b

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Aug 10, 2020

The instructor did a great job!

创建者 Sukrut B

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Oct 19, 2020

Try to make it little bit easy

创建者 Javas A B Y P

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Mar 28, 2021

Alhamdulillah, this is great!

创建者 Israel d S R d A

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Jun 5, 2020

Great course very recommended

创建者 Muhammad T

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Mar 2, 2021

haha good course i completed

创建者 Jonah L

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Dec 6, 2020

It's hard but it's worth it!

创建者 Gautham T

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Jun 15, 2019

excellent course by imperial

创建者 Ankur A

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May 15, 2020

Tough course, learnt a lot.

创建者 Ajay S

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Feb 20, 2021

Great course for every one

创建者 Felix G S S

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Mar 27, 2021

Wow, it is so challenging

创建者 Ricardo C V

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Dec 25, 2019

Challenging but Excellent

创建者 CHAITANYA V

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Jul 16, 2020

Excellent course content

创建者 Mayank K

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Jul 2, 2020

This course is very good

创建者 Nihal T

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Jul 13, 2022

Amazing Specialization!

创建者 Michael M

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Aug 3, 2021

I strongly recommend it

创建者 Subhodip P

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Dec 15, 2020

Awesome course loved it

创建者 Pranav N

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Aug 25, 2020

Amazing overall course