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学生对 Imperial College London 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

4.0
3,155 个评分

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 16, 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.

WS

Jul 6, 2021

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.

筛选依据:

701 - Mathematics for Machine Learning: PCA 的 725 个评论(共 791 个)

创建者 Ivo R

Nov 16, 2019

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

创建者 raghu c

Apr 4, 2020

Needs to demo a little bit of code owing to the complexity of the course content.Lectures gives just a high level understanding only. Assignments are taking far more complicated than expected.

创建者 Paulo H S G

Nov 27, 2020

Even though the videos and quizzes are well produced and informative, the assignments are so poorly designed that they can only bring about some frustration with the learning process.

创建者 Yi S

Jun 11, 2021

assignment and quiz are not well designed. the knowledge covered in lectures are not enough to complete assignments. The first two courses in this specialization is much better.

创建者 Nidhi G

Aug 23, 2020

Faced a lot of problems in exercises. Don't feel that i have completely understood the concepts. This course can be made more learner friendly with better explanations.

创建者 Tushar G

Feb 9, 2023

not a good course for beginners in machine learning, concepts need to be explained more clearly. Other courses in this specialization were way better than this one.

创建者 vignesh n

Sep 12, 2018

Explaination of many things are skipped, assumption was made by the instructor that lot of things were already known by the learner. It could have been much better.

创建者 Maksim S

Mar 24, 2020

The difficulty of the course is inadequate and the pace is not balanced. Requires a lot of search for additional resources to understand materials. I cancelled.

创建者 Ghanem A

Jul 20, 2021

Response to questions is very slow. Support to learners is not sufficient

Programming assignments are not explained well (some I believe have errors)

创建者 Kovendhan V

Jul 11, 2020

After first two amazing courses in this specialisation, third course was a huge let down. One skill I learnt from this last course is patience.

创建者 Martin H

Dec 8, 2019

Lack of examples to clarify abstract concepts. Big contrast in quality compared to the other courses in this specialization.

创建者 Jamiul H D

Aug 7, 2020

Poor explanation by the instructor. Previous ones were very helpful. I didn't understand many topics well

创建者 Lavanith T

Aug 21, 2020

Everything is okay but there is a huge drawback with the programming explanation part.

创建者 Xiao L

Jun 3, 2019

very wired assignment, a lot of error in template code. The concept is not clear.

创建者 Sai M B

Aug 2, 2020

The lectures were not clear. I had to use other sources to understand lectures.

创建者 Pawan K S

Jun 20, 2020

This course was the hardest I encountered in this specialisation.

创建者 Mohamed A H

Aug 18, 2021

it was not clear alot of the time and it was really hard

创建者 Kirill T

Jul 26, 2020

Way worse than the previous courses. Lacks explanations

创建者 Kevin O

Mar 27, 2021

Really interesting topic but not nearly enough detail.

创建者 Amr F M R

Sep 22, 2020

I think course material was not explained well at all.

创建者 Timothy M

Apr 21, 2021

The lectures and assignments did not synergize well.

创建者 Aravind

Sep 23, 2019

Need to improve the content and delivery of content.

创建者 Mohammed A A

Jul 19, 2020

the course is too shallow with difficult code exame

创建者 Scoodood C

Jul 27, 2018

Video lecture not as intuitive as previous courses.

创建者 Michael B

Nov 21, 2019

Programming assignments not well explained