Volver a Principal Component Analysis with NumPy

## Opiniones y comentarios de aprendices correspondientes a Principal Component Analysis with NumPy por parte de Coursera Project Network

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## Acerca del Curso

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, youâ€™ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed....

## Principales reseÃ±as

TS

4 de oct. de 2020

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TA

30 de oct. de 2020

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

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## 1 - 25 de 48 revisiones para Principal Component Analysis with NumPy

por Rishit C

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1 de jun. de 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.

por Pranav D

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19 de jun. de 2020

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

por Karina R B

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10 de sep. de 2020

Muy buena explicaciÃ³n para cada uno de los aspectos del PCA.

por Zixiang M

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11 de jun. de 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.

por Tanuj A

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31 de oct. de 2020

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

por Hector P

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9 de sep. de 2020

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

por Mayank S

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24 de abr. de 2020

Learned Applying PCA

Concise course.

Liked the method of teaching.

por Jose A

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26 de jul. de 2020

Good Exercise to practice and understand a little better.

por LIN F

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4 de nov. de 2020

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

por VIJAY K

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18 de jul. de 2020

Instructor is amazing, explains the things very well

por Dr.T.Hemalatha c

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9 de jun. de 2020

simple and an elegant example to understand

por Jayasanthi

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25 de abr. de 2020

Very good explanation with demo. Thank you.

por Dr. C S G

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9 de jun. de 2020

This course is very useful in learning PCA

por Punam P

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12 de may. de 2020

Nice and Helpful course...Thanks to Team

por Prajwal K

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11 de nov. de 2020

Thanks a lot Snehan .Learned a lot .

por Dr. P W

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31 de may. de 2020

This is good course for beginners

por Syed A R

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3 de nov. de 2020

Excellent course and instructor.

por Sitesh R

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28 de jun. de 2020

The couse was made very simple.

por ENRICA M M

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27 de may. de 2020

Corso davvero utile e semplice.

por Oscar A C B

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12 de jun. de 2020

Just as simple as I needed!

por ANURAG P

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14 de jul. de 2020

Great course for beginners

por TUSHAR S

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5 de oct. de 2020

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por rishabh m t

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25 de sep. de 2020

highly informative

por Gangone R

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3 de jul. de 2020

very useful course

por Kamol D D

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18 de abr. de 2020

Very Satisfactory