# 學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

4.0
2,600 個評分
645 條評論

## 課程概述

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....

## 熱門審閱

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.

NS
2020年6月18日

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

## 151 - Mathematics for Machine Learning: PCA 的 175 個評論（共 641 個）

2018年11月25日

This course demystifies the Principal Components Analysis through practical implementation. It gives me solid foundations for learning further data science techniques.

2020年4月3日

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.

2020年10月31日

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

2020年5月3日

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

2018年4月14日

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

2020年4月20日

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

2018年6月24日

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

2020年11月21日

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

2019年3月21日

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

2020年12月11日

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

2020年9月24日

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

2019年11月24日

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

2018年12月30日

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

2020年9月24日

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

2018年7月30日

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

2020年10月21日

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

2020年2月20日

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

2020年6月17日

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

2020年3月10日

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

2020年5月14日

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

2019年2月3日

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

2019年9月18日

This course is very helpful for me to understand Math for ML. Thank you Professors at Imperial College London so much!

2020年5月24日

Professor Deisenroth is amazing. Very tough course but appreciated all the derivations and explanations of concepts.

2019年3月21日

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.

2018年9月28日

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.