Chevron Left
Principal Component Analysis with NumPy(으)로 돌아가기

Coursera Project Network의 Principal Component Analysis with NumPy 학습자 리뷰 및 피드백

4.6
별점
153개의 평가
26개의 리뷰

강좌 소개

Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed....

최상위 리뷰

필터링 기준:

Principal Component Analysis with NumPy의 27개 리뷰 중 1~25

교육 기관: Rishit C

Jun 01, 2020

Some places the code used could have been simplified to be easier for the learner to understand. For example: (eigen_vectors.T[:][:])[:2].T was used in the course video but it can be replaced by eigen_vectors[:, :2]. The second one which I used is much simpler and cleaner to understand.

Thank You.

교육 기관: Pranav D

Jun 19, 2020

Did not focus on the mathematics part of PCA. The explanation could have been better and easy to understand.

교육 기관: Zixiang M

Jun 12, 2020

The platform is really hard to use, the screen is small, and there're lags when I'm typing into the jupyter notebook on the virtual desktop.

교육 기관: Mayank S

Apr 25, 2020

Learned Applying PCA

Concise course.

Liked the method of teaching.

교육 기관: Dr.T.Hemalatha c

Jun 09, 2020

simple and an elegant example to understand

교육 기관: Jayasanthi

Apr 25, 2020

Very good explanation with demo. Thank you.

교육 기관: Dr. C S G

Jun 09, 2020

This course is very useful in learning PCA

교육 기관: PATIL P R

May 12, 2020

Nice and Helpful course...Thanks to Team

교육 기관: Dr. P W

May 31, 2020

This is good course for beginners

교육 기관: Sitesh R

Jun 28, 2020

The couse was made very simple.

교육 기관: ENRICA M M

May 27, 2020

Corso davvero utile e semplice.

교육 기관: Oscar A C B

Jun 12, 2020

Just as simple as I needed!

교육 기관: Gangone R

Jul 03, 2020

very useful course

교육 기관: Kamol D D

Apr 18, 2020

Very Satisfactory

교육 기관: Hari O U

Apr 19, 2020

Great experience

교육 기관: Abhishek P G

Jun 15, 2020

satisfied

교육 기관: Kamlesh C

Jul 08, 2020

Thanks

교육 기관: p s

Jun 29, 2020

Good

교육 기관: tale p

Jun 28, 2020

good

교육 기관: Vajinepalli s s

Jun 16, 2020

nice

교육 기관: Vipul P

Jun 14, 2020

The course felt a bit too short and the time allotted for the guided project was barely enough to complete it in time leaving little to no room for thinking and rewinding the videos which made it a bit uncomfortable to take.

교육 기관: Prashant P

Jun 01, 2020

Course is amazing, got many concepts clear, learned a lot. Would also be great if more than one datasets are taken as excercise.

교육 기관: Sumit S

Jun 01, 2020

It was quite conceptional but the instructor made it easy for me to implement and follow along.

교육 기관: Ashutosh S T

May 09, 2020

Excellence experiece, good content for begineers, thanx coursera.