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Mathematics for Machine Learning: PCA(으)로 돌아가기

임페리얼 칼리지 런던의 Mathematics for Machine Learning: PCA 학습자 리뷰 및 피드백

2,810개의 평가
698개의 리뷰

강좌 소개

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....

최상위 리뷰


2021년 7월 6일

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.


2018년 7월 16일

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.

필터링 기준:

Mathematics for Machine Learning: PCA의 695개 리뷰 중 676~695

교육 기관: Daniel C

2021년 8월 20일

​the lecture videos do not seem to provide enough guidance for the assignments

교육 기관: TUSHAR K

2020년 7월 19일

Previous Two Courses were better in terms of both assignments and teaching.

교육 기관: Siddharth S

2020년 6월 4일

Very Poor when compared to previous two courses of this specialization.

교육 기관: Saeif A

2020년 1월 1일

This course was a disaster for me. The first two were great though.

교육 기관: Jared E

2018년 8월 25일

Impossible to do without apparently an indepth knowledge of python.

교육 기관: Soumitri C

2020년 12월 15일

okayish teaching but grading system is absolute rubbish in Week4

교육 기관: Aditya P

2020년 7월 4일

Very poor teaching and overall it's the worst course I've taken

교육 기관: Ahmad O

2020년 8월 27일

Very bad explanation. The assignments need more instructions.

교육 기관: Aurel N

2020년 7월 5일

k-NN assignment is full of errors and no proper explanations.

교육 기관: Wensheng Z

2019년 11월 24일

Jumpy instruction with little illustrations

교육 기관: Adam C

2019년 10월 31일

Worst course I've ever taken, online or IRL

교육 기관: Zecheng W

2019년 10월 19일

Poorly organized and extremely confusing

교육 기관: Mingzhe D

2019년 12월 11일

Assignment 1 cannot be passed!

교육 기관: ML-07 C k

2021년 3월 2일

confuse , difficuld and weird

교육 기관: 朱嘉懿

2020년 6월 25일

The assignment worked badly.

교육 기관: Syed s A

2020년 7월 23일

Assignment is not proper

교육 기관: Анофриев А

2019년 10월 1일

The worst course ever

교육 기관: Bohdan S

2020년 2월 17일

Worst course ever

교육 기관: Ankit M

2020년 7월 12일


교육 기관: Arjunsiva S

2020년 10월 4일