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Mathematics for Machine Learning: PCA に戻る

インペリアル・カレッジ・ロンドン(Imperial College London) による Mathematics for Machine Learning: PCA の受講者のレビューおよびフィードバック

4.0
2,819件の評価
702件のレビュー

コースについて

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....

人気のレビュー

WS

2021年7月6日

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

2018年7月16日

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.

フィルター:

Mathematics for Machine Learning: PCA: 251 - 275 / 700 レビュー

by Phani B R P

2020年6月1日

Very good course and extremely challenging, especially PCA

by Anh V

2020年11月15日

Very detailed explanation and mathematics underlying PCA!

by Md. A A M

2020年8月24日

Great Course. Everyone should take this course. Thanks.

by Harish S

2019年11月24日

This was a difficult course but still very informative.

by Oleg B

2019年1月6日

Excellent focus on important topics that lead up to PCA

by Kaustubh S

2020年11月29日

Very tough course but got a good sense of what PCA is

by Prateek S

2020年7月14日

best course and important to study with concentration

by Lahiru D

2019年9月16日

Great course. Assignments are tough and challenging.

by Archana D

2020年3月6日

Brilliant work, references and formulas aided a lot

by Tich M

2019年1月18日

good course, rigorous proof and practical exercises

by Goh K L

2021年8月8日

Decently challenging and therefore very fruitful.

by Diego S

2018年5月2日

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

by Ida B R A M M

2022年3月27日

Very HARD but fundamentals are important, yes?

by CHIOU Y C

2020年2月3日

A good representation after preceding courses.

by Wang S

2019年10月21日

A little bit difficult but helpful, thank you!

by eder p g

2020年8月9日

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

by Murugesan M

2020年1月15日

Excellent! very intuitive learning approach!!

by Md. F I

2022年6月8日

G​ood. But Programming exercise is not clear

by Hritik K S

2019年6月20日

Maths is just like knowing myself very well!

by K A K

2020年5月22日

Learnt many new things I didn't know before

by Naggita K

2018年12月19日

Great course. Rich well explained material.

by Sivasankar S

2021年8月3日

This course is very informative and useful

by Carlos E G G

2020年9月28日

Really difficult, but worth it in the end.

by Zongrui H

2021年5月11日

PCA assignment in week4 is a chanllenge!

by Binu V P

2020年6月8日

best course I had ever done in coursera