Chevron Left
Principal Component Analysis with NumPy に戻る

Coursera Project Network による Principal Component Analysis with NumPy の受講者のレビューおよびフィードバック

4.6
278件の評価
47件のレビュー

コースについて

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....

人気のレビュー

TS
2020年10月4日

V\n\ne\n\nr\n\ny\n\ng\n\no\n\no\n\nd\n\nG\n\nu\n\ni\n\nd\n\ne\n\nd\n\ne\n\np\n\nr\n\no\n\nj\n\ne\n\nc\n\nt

TA
2020年10月30日

Good Introductory project to gain insights into PCA using Numpy and python.

フィルター:

Principal Component Analysis with NumPy: 1 - 25 / 47 レビュー

by Rishit C

2020年6月1日

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.

by Pranav D

2020年6月19日

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

by Karina R B

2020年9月10日

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

by Zixiang M

2020年6月11日

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.

by Tanuj A

2020年10月31日

Good Introductory project to gain insights into PCA using Numpy and python.

by Hector P

2020年9月9日

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

by Mayank S

2020年4月24日

Learned Applying PCA

Concise course.

Liked the method of teaching.

by Jose A

2020年7月26日

Good Exercise to practice and understand a little better.

by LIN F

2020年11月4日

It's clear for the new learner to follow up. Thank you.

by VIJAY K

2020年7月18日

Instructor is amazing, explains the things very well

by Dr.T.Hemalatha c

2020年6月9日

simple and an elegant example to understand

by Jayasanthi

2020年4月25日

Very good explanation with demo. Thank you.

by Dr. C S G

2020年6月9日

This course is very useful in learning PCA

by Punam P

2020年5月12日

Nice and Helpful course...Thanks to Team

by Prajwal K

2020年11月11日

Thanks a lot Snehan .Learned a lot .

by Dr. P W

2020年5月31日

This is good course for beginners

by Syed A R

2020年11月3日

Excellent course and instructor.

by Sitesh R

2020年6月28日

The couse was made very simple.

by ENRICA M M

2020年5月27日

Corso davvero utile e semplice.

by Oscar A C B

2020年6月12日

Just as simple as I needed!

by ANURAG P

2020年7月14日

Great course for beginners

by TUSHAR S

2020年10月5日

V

e

r

y

g

o

o

d

G

u

i

d

e

d

e

p

r

o

j

e

c

t

by rishabh m t

2020年9月25日

highly informative

by Gangone R

2020年7月3日

very useful course

by Kamol D D

2020年4月18日

Very Satisfactory