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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

Оценки: 281
Рецензии: 47

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


4 окт. 2020 г.



30 окт. 2020 г.

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

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1–25 из 47 отзывов о курсе Principal Component Analysis with NumPy

автор: Rishit C

1 июня 2020 г.

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

19 июня 2020 г.

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

автор: Karina R B

10 сент. 2020 г.

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

автор: Zixiang M

11 июня 2020 г.

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: Tanuj A

31 окт. 2020 г.

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

автор: Hector P

9 сент. 2020 г.

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

автор: Mayank S

24 апр. 2020 г.

Learned Applying PCA

Concise course.

Liked the method of teaching.

автор: Jose A

26 июля 2020 г.

Good Exercise to practice and understand a little better.

автор: LIN F

4 нояб. 2020 г.

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

автор: VIJAY K

18 июля 2020 г.

Instructor is amazing, explains the things very well

автор: Dr.T.Hemalatha c

9 июня 2020 г.

simple and an elegant example to understand

автор: Jayasanthi

25 апр. 2020 г.

Very good explanation with demo. Thank you.

автор: Dr. C S G

9 июня 2020 г.

This course is very useful in learning PCA

автор: Punam P

12 мая 2020 г.

Nice and Helpful course...Thanks to Team

автор: Prajwal K

11 нояб. 2020 г.

Thanks a lot Snehan .Learned a lot .

автор: Dr. P W

31 мая 2020 г.

This is good course for beginners

автор: Syed A R

3 нояб. 2020 г.

Excellent course and instructor.

автор: Sitesh R

28 июня 2020 г.

The couse was made very simple.

автор: ENRICA M M

27 мая 2020 г.

Corso davvero utile e semplice.

автор: Oscar A C B

12 июня 2020 г.

Just as simple as I needed!

автор: ANURAG P

14 июля 2020 г.

Great course for beginners

автор: TUSHAR S

5 окт. 2020 г.























автор: rishabh m t

25 сент. 2020 г.

highly informative

автор: Gangone R

3 июля 2020 г.

very useful course

автор: Kamol D D

18 апр. 2020 г.

Very Satisfactory