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Mathematics for Machine Learning: PCA(으)로 돌아가기

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

4.0
별점
2,710개의 평가
677개의 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

WS
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.

JS
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 675개 리뷰 중 251~275

교육 기관: Lahiru D

2019년 9월 16일

Great course. Assignments are tough and challenging.

교육 기관: Archana D

2020년 3월 6일

Brilliant work, references and formulas aided a lot

교육 기관: Tich M

2019년 1월 18일

good course, rigorous proof and practical exercises

교육 기관: Goh K L

2021년 8월 8일

Decently challenging and therefore very fruitful.

교육 기관: Diego S

2018년 5월 2일

Difficult! But I did it :D And I learnt a lot...

교육 기관: CHIOU Y C

2020년 2월 3일

A good representation after preceding courses.

교육 기관: Wang S

2019년 10월 21일

A little bit difficult but helpful, thank you!

교육 기관: eder p g

2020년 8월 9일

excellent!!!! it's very useful and practical.

교육 기관: Murugesan M

2020년 1월 15일

Excellent! very intuitive learning approach!!

교육 기관: Hritik K S

2019년 6월 20일

Maths is just like knowing myself very well!

교육 기관: K A K

2020년 5월 22일

Learnt many new things I didn't know before

교육 기관: Naggita K

2018년 12월 19일

Great course. Rich well explained material.

교육 기관: Sivasankar S

2021년 8월 3일

This course is very informative and useful

교육 기관: Carlos E G G

2020년 9월 28일

Really difficult, but worth it in the end.

교육 기관: Zongrui H

2021년 5월 11일

PCA assignment in week4 is a chanllenge!

교육 기관: Binu V P

2020년 6월 8일

best course I had ever done in coursera

교육 기관: Jonathon K

2020년 4월 13일

Great course. Extremely smart lecturer.

교육 기관: Xi C

2018년 12월 31일

Great course. Cover rigorous materials.

교육 기관: Akshay K

2019년 1월 25일

This was a tough course. But worth it.

교육 기관: Carlos A V P

2022년 1월 15일

E​xcelente curso, muy claro y retador

교육 기관: Wassana K

2021년 3월 22일

Programming Assignment is so hard !!!

교육 기관: THIRUPATHI T

2020년 5월 24일

Thank you for offering a nice course.

교육 기관: Eli C

2018년 7월 21일

very challenging and rewarding course

교육 기관: Indria A

2021년 3월 26일

very very tiring but fun, thank you.

교육 기관: Jeff D

2020년 11월 1일

Thank you very much for this course.