返回到 Mathematics for Machine Learning: PCA

4.0

星

2,161 個評分

•

535 條評論

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....

JS

Jul 17, 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.

NS

Jun 19, 2020

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.

篩選依據：

創建者 Prime S

•Jun 24, 2018

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

創建者 J A M

•Mar 21, 2019

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

創建者 Sateesh K

•Sep 24, 2020

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

創建者 Moez B

•Nov 25, 2019

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

創建者 Hasan A

•Dec 31, 2018

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

創建者 Duy P

•Sep 24, 2020

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

創建者 Alexander H

•Jul 31, 2018

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

創建者 Prabal G

•Oct 21, 2020

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

創建者 Jason N

•Feb 20, 2020

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

創建者 Rishabh P

•Jun 17, 2020

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

創建者 UMAR T

•Mar 10, 2020

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

創建者 Josef N

•May 14, 2020

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

創建者 Dora J

•Feb 04, 2019

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

創建者 Trung T V

•Sep 19, 2019

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

創建者 Mukund M

•May 24, 2020

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

創建者 David H

•Mar 21, 2019

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

創建者 Lee F

•Sep 28, 2018

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

創建者 Nileshkumar R P

•May 06, 2020

This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.

創建者 Nishek S

•Jul 30, 2020

The PCA part Was a bit tricky barely handle the concepts.

thank you imperial team for such interactive course

創建者 Krzysztof

•Aug 21, 2019

One of the most challenging course in my life - almost impossible without python and mathematics background.

創建者 Pratama A A

•Aug 26, 2020

Need more Effort to grasp the materials explained_-" you need to be patience,the lecturer is really on top

創建者 Nelson S S

•Jul 29, 2020

Excellent course ... Quite challenging, a little difficult but I have learned a lot ... Thank you ...

創建者 sameen n

•Sep 06, 2019

Amazing course and provides basic introduction for the PCA. Need for programming help in this course.

創建者 Brian H

•Feb 25, 2020

Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.

創建者 Natalya T

•Feb 25, 2019

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

- Finding Purpose & Meaning in Life
- Understanding Medical Research
- Japanese for Beginners
- Introduction to Cloud Computing
- Foundations of Mindfulness
- Fundamentals of Finance
- 機器學習
- 使用 SAS Viya 進行機器學習
- 幸福科學
- Covid-19 Contact Tracing
- 適用於所有人的人工智能課程
- 金融市場
- 心理學導論
- Getting Started with AWS
- International Marketing
- C++
- Predictive Analytics & Data Mining
- UCSD Learning How to Learn
- Michigan Programming for Everybody
- JHU R Programming
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- AI for Medicine
- Good with Words: Writing & Editing
- Infections Disease Modeling
- The Pronounciation of American English
- Software Testing Automation
- 深度學習
- 零基礎 Python 入門
- 數據科學
- 商務基礎
- Excel 辦公技能
- Data Science with Python
- Finance for Everyone
- Communication Skills for Engineers
- Sales Training
- 職業品牌管理職業生涯品牌管理
- Wharton Business Analytics
- Penn Positive Psychology
- Washington Machine Learning
- CalArts Graphic Design

- 專業證書
- MasterTrack 證書
- Google IT 支持
- IBM 數據科學
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI Applied Project Management
- Instructional Design Certificate
- Construction Engineering and Management Certificate
- Big Data Certificate
- Machine Learning for Analytics Certificate
- Innovation Management & Entrepreneurship Certificate
- Sustainabaility and Development Certificate
- Social Work Certificate
- AI and Machine Learning Certificate
- Spatial Data Analysis and Visualization Certificate

- Computer Science Degrees
- Business Degrees
- 公共衛生學位
- Data Science Degrees
- 學士學位
- 計算機科學學士
- MS Electrical Engineering
- Bachelor Completion Degree
- MS Management
- MS Computer Science
- MPH
- Accounting Master's Degree
- MCIT
- MBA Online
- 數據科學應用碩士
- Global MBA
- Master's of Innovation & Entrepreneurship
- MCS Data Science
- Master's in Computer Science
- 公共健康碩士