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学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

4.0
1,910 个评分
455 条评论

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 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.

筛选依据:

201 - Mathematics for Machine Learning: PCA 的 225 个评论(共 455 个)

创建者 Andrey

Sep 17, 2018

Great course!

创建者 Samresh

Aug 10, 2019

Nice Course.

创建者 David N

Jul 24, 2019

Great course

创建者 Manikant R

Jun 08, 2020

Best course

创建者 Salah T

Apr 26, 2020

Many thanks

创建者 Artur

Feb 29, 2020

good course

创建者 Mohamed H

Aug 10, 2019

fantastic

创建者 Karthik

May 03, 2018

RRhis cl

创建者 Akash G

Mar 20, 2019

awesome

创建者 Bálint - H F

Mar 20, 2019

Great !

创建者 GEETHA P

Jul 28, 2020

good

创建者 RAGHUVEER S D

Jul 25, 2020

good

创建者 HARSH K D

Jun 28, 2018

good

创建者 Ertuğrul G

Jun 07, 2020

The overall experience was very good. I have enjoyed all the math in videos and PCA derivation throughout the course. The course a bit harder than the previous ones in the specialization. However after some effort one can understand the points that is not taught thoroughly. Only downside of the course is the programming environment. I have attended different courses that are also using Jupyter notebooks on Coursera and they were flawless. Here we have, some cells do run forever, a grader behaving inconsistently and one week that has some steps completely against the general software engineering principles. By the way discussion forums are so helpful and make me understand some math concepts on the way. I recommend the course to people who want to improve their understanding of math before deep diving machine learning courses.

创建者 Niju M N

Apr 09, 2020

This is the final course in the Specialization, that focuses on Principal component Analysis.This course is a bit hard compared to the other two courses in specialization. This builds on the topics explained in the other two courses.The Instructor tries to squeeze the concepts in the limited time.Not all materials are completely explained in the video, however, students can refer to other materials available in the web/ Refer the course forums and get the concepts and use them to solve the Quizzes. Some times the Assignments and quizzes are frustrating , however they do a good job of reinforcing the ideas taught in the video. Totally this is a good time spent .

创建者 Vassiliy T

Jul 10, 2018

it is good, challenging course. i've learned a lot, but feel that i came away with quite patchy knowledge. This course is a big step up in complexity and delivery form the previous two courses. perhaps my expectations were not right to start with - one cannot learn this level of complexity so quickly. Admittedly there are many gaps between the lectures and course materials and what is asked in programming assignments. i ended up reading a lot online to fill in the gaps (i've learned a lot of python during the course, which is great!).nevertheless, after this course i feel equipped to continue with machine learning.

创建者 Matteo L

Apr 20, 2020

I think this course is slightly underrated at the moment. The topic is not an easy one and I thought the teacher did a great job of explaining it as clearly as possible using an appropriate amount of mathematical derivation.

I really thought the last week of the course was great, especially considering that everything we had seen so far in the specialization was used to develop the PCA algorithm. It's quite amazing how topics such as eigenvectors, projections and optimization all come together here.

I think the notebooks were quite challenging compared to the previous two courses with is definitely a plus!

创建者 Nikolay B

Aug 03, 2019

Instructor gives the very dry but useful essence of the "philosophical" concepts of dot and generalized inner product, etc., - personally, liked that. Unfortunately, the offered problems are so far away from the delivered videos but the web search helps on getting the hints. This course makes you think - I learned a lot just by asking myself "what do they mean under this statement?", what they want in this task? Though I will appreciate if providers elaborate the material further and so instead of googling we spend our time watching - a single point access.

创建者 Luke L

Jun 11, 2020

This course was a very interesting end to the specialization. The first two courses teach you the tools necessary, while this course teaches practical (although highly theoretical) application of those tools. The reason for the non-five star rating is the python exercises take one on quite a journey, which often times goes through some dark and murky tunnels which are tough to escape. Also, there are few worked examples during lectures.

创建者 Antonio C P

Jan 24, 2020

It was a real tour de force on the mathematics, and I had some hard time following the ideas of the instructor many times. However, the topic was completely covered in a very systematic way, which is excellent in my opinion. My only suggestion is to focus more on what really matters: do we really have to spend such a long time discussing about different metrics for an inner product if in the end we only use the euclidean metric?

创建者 Rob O

Aug 03, 2020

An in-depth exploration of the PCA algorithm and the math behind it. Python programming exercises helped me to solidify the theory and derivations. Numpy is used extensively in the exercises and I liked the experience that I gained in applying it. I found that the connection between the exercises and theory were not always clearly drawn and needed to refer to the discussion to fill in the gaps.

创建者 Barnaby D

Jan 03, 2020

Would give this course 5 stars if it was properly described so that expectation could match reality:

Give yourself plenty of time for this course - it will take quite a bit longer than described.

Make sure you are comfortable with Python and NumPy before you start (particularly the linear algebra functions).

It is very different (much less hand-holding) than the other courses in the specialization.

创建者 Nelson F A

Apr 25, 2019

This course brings together many of the concepts from the first two courses of the specialization. If you worked through them already, then this course is a must. There are some issues with the programming assignments and the lectures could do with some more practical examples. Be sure to check the discussions forums for help. For me they were essential to passing the course.

创建者 Visveswara K M

Jun 18, 2020

This was a bit more challenging than the previous two courses. I didn't enjoy it as much as the previous courses, however, I learnt more than the previous two. The discussion forums were helpful and the instructors contributed regularly. The assignments were a bit frustrating at times but still manageable. However, the assignments could have had a bit more of explanations.

创建者 greg m

May 24, 2020

Very good course, interesting material. However the amount of programming knowledge required is way beyond a beginner like myself and I struggled with that , consuming much time. Those with programming knowledge have a tremendous advantage on this course.

There should be a week or a separate brief course on python/numpy.

A follow up more advanced course would be good too.