Mathematics for Machine Learning: PCA(으)로 돌아가기

4.0
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2,835개의 평가

## 강좌 소개

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....

## 최상위 리뷰

WS

2021년 7월 6일

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.

JS

2018년 7월 16일

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.

필터링 기준:

## Mathematics for Machine Learning: PCA의 706개 리뷰 중 176~200

교육 기관: XL T

2020년 4월 3일

It is a bit difficult and jumpy. You will need some hard work to fill in the missing links of knowledge which not explicite on the lectrue. Overall, great experience.

교육 기관: Camilo V F

2022년 7월 20일

Really clear and well explained. The concepts are treated in detail enough to be applied. Very happy to have invested my time in this course. I strongly recomend it.

교육 기관: Fabrizio B

2020년 10월 31일

Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!

교육 기관: Prut S

2021년 8월 16일

The content was challenging but very well structured. It is nice to understand the mathematics behind it rather than just blindly using PCA in your projects.

교육 기관: S J

2020년 5월 3일

Your Teaching and Video quality is par excellence.....Thanks a lot for such amazing stuff...I am looking forward to joining more courses in the same line

교육 기관: Bui V H D

2021년 12월 16일

I think it is the best hard in 3 course of the series, but It give many new knowlegde and build a mindset with math for machine learning.

Great Course!

교육 기관: Christine D

2018년 4월 14일

I found this course really excellent. Very clear explanations with very hepful illustrations.

I was looking for course on PCA, thank you for this one

교육 기관: Ananta M

2020년 4월 20일

Although the course was little out there and the instructor was trying his best to articulate a difficult topic, the overall experience is great.

교육 기관: qwer q

2018년 6월 24일

Nicely explained. Could be further improved by adding some noted or sources of derivation of some expressions, like references to matrix calculus

교육 기관: Xiaoou W

2020년 11월 21일

great content however the programming part is too challenging for people without propre guidance in the subject. the videos aren't of much help.

교육 기관: J A M

2019년 3월 21일

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

교육 기관: Amar n

2020년 12월 11일

Just Brilliant!!! Very well structured with very clear assignments. Doing the assignments is a must if you want to get clarity on the subject.

교육 기관: Sateesh K

2020년 9월 24일

This course should be part of "gems of coursera". Excellent specialization, thoroughly enjoyed it. For me the 3rd course on PCA was the best.

교육 기관: Moez B

2019년 11월 24일

Excellent course. The fourth week material is the hardest for folks not comfortable with linear algebra and vectorization in numpy and scipy.

교육 기관: Hasan A

2018년 12월 30일

What a great opportunity this course offers to learn from the best in this simplified manner. Thank you Coursera and Imperial College London!

교육 기관: Duy P

2020년 9월 24일

Excellent explanation from the professor!! Besides he is the author of the book Mathematics for Machine Learning. You should check it out.

교육 기관: Alexander H

2018년 7월 30일

Highly informative course! Loved the depth of the material. Found this course content highly useful in my current project based on PCA.

교육 기관: Golnaz

2021년 10월 29일

I liked how practical this course was. The programming assignments were really beneficial for a deeper understanding of the material.

교육 기관: Prabal G

2020년 10월 21일

great course for mathematics and machine learning...A big thanks to my faculty to guide like a god in this applied mathematics course

교육 기관: Jason N

2020년 2월 20일

A lot of reading beyond the video lectures was required for me and some explanations could be more clear. Overall, a great course.

교육 기관: Rishabh P

2020년 6월 17일

Well-detailed course and straight to the point. I enjoyed the course even though the programming assignments can be challenging

교육 기관: UMAR T

2020년 3월 10일

Excellent course it helps you understanding about linear algebra programming into real world examples by programming in python.

교육 기관: Giorgio B

2022년 3월 18일

T​he leadup to PCA was needed and thought clear. I now have a better understand of how projections and inner products work.

교육 기관: Josef N

2020년 5월 14일

It would be great if the course is extended to 8 weeks, with the current week 4 spanning at least 3 weeks. Otherwise great.

교육 기관: Teiichi A

2021년 8월 5일

C​hallenging, with a lot to fill between the topics. Was shown how much further I can learn, which I am really grateful.