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Вернуться к Principal Component Analysis with NumPy

Отзывы учащихся о курсе Principal Component Analysis with NumPy от партнера Coursera Project Network

4.6
звезд
Оценки: 223
Рецензии: 38

О курсе

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....

Лучшие рецензии

HP

Sep 09, 2020

This is a great project. The instructor facilitates clear and practically.

MS

Apr 25, 2020

Learned Applying PCA\n\nConcise course.\n\nLiked the method of teaching.

Фильтр по:

1–25 из 38 отзывов о курсе Principal Component Analysis with NumPy

автор: 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.

автор: Pranav D

Jun 19, 2020

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

автор: 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.

автор: Hector P

Sep 09, 2020

This is a great project. The instructor facilitates clear and practically.

автор: Mayank S

Apr 25, 2020

Learned Applying PCA

Concise course.

Liked the method of teaching.

автор: Karina R B

Sep 10, 2020

Muy buena explicación para cada uno de los aspectos del PCA.

автор: Jose A

Jul 26, 2020

Good Exercise to practice and understand a little better.

автор: VIJAY K

Jul 18, 2020

Instructor is amazing, explains the things very well

автор: Dr.T.Hemalatha c

Jun 09, 2020

simple and an elegant example to understand

автор: Jayasanthi

Apr 25, 2020

Very good explanation with demo. Thank you.

автор: Dr. C S G

Jun 09, 2020

This course is very useful in learning PCA

автор: Punam P

May 12, 2020

Nice and Helpful course...Thanks to Team

автор: Dr. P W

May 31, 2020

This is good course for beginners

автор: Sitesh R

Jun 28, 2020

The couse was made very simple.

автор: ENRICA M M

May 27, 2020

Corso davvero utile e semplice.

автор: Oscar A C B

Jun 12, 2020

Just as simple as I needed!

автор: ANURAG P

Jul 14, 2020

Great course for beginners

автор: Gangone R

Jul 03, 2020

very useful course

автор: Kamol D D

Apr 18, 2020

Very Satisfactory

автор: Hari O U

Apr 19, 2020

Great experience

автор: ELANGOVAN K

Jul 21, 2020

Good project

автор: ARUNAVA B

Aug 14, 2020

excellent.

автор: SASI V T

Jul 13, 2020

EXCELLENT

автор: Abhishek P G

Jun 15, 2020

satisfied

автор: Kamlesh C

Jul 08, 2020

Thanks