Chevron Left
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

4.6
estrellas
228 calificaciones
39 reseña

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, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed....

Principales reseñas

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.

Filtrar por:

1 - 25 de 39 revisiones para Principal Component Analysis with NumPy

por 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.

por Pranav D

Jun 19, 2020

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

por 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.

por Hector P

Sep 09, 2020

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

por Mayank S

Apr 25, 2020

Learned Applying PCA

Concise course.

Liked the method of teaching.

por Karina R B

Sep 10, 2020

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

por Jose A

Jul 26, 2020

Good Exercise to practice and understand a little better.

por VIJAY K

Jul 18, 2020

Instructor is amazing, explains the things very well

por Dr.T.Hemalatha c

Jun 09, 2020

simple and an elegant example to understand

por Jayasanthi

Apr 25, 2020

Very good explanation with demo. Thank you.

por Dr. C S G

Jun 09, 2020

This course is very useful in learning PCA

por Punam P

May 12, 2020

Nice and Helpful course...Thanks to Team

por Dr. P W

May 31, 2020

This is good course for beginners

por Sitesh R

Jun 28, 2020

The couse was made very simple.

por ENRICA M M

May 27, 2020

Corso davvero utile e semplice.

por Oscar A C B

Jun 12, 2020

Just as simple as I needed!

por ANURAG P

Jul 14, 2020

Great course for beginners

por rishabh m t

Sep 25, 2020

highly informative

por Gangone R

Jul 03, 2020

very useful course

por Kamol D D

Apr 18, 2020

Very Satisfactory

por Hari O U

Apr 19, 2020

Great experience

por ELANGOVAN K

Jul 21, 2020

Good project

por ARUNAVA B

Aug 14, 2020

excellent.

por SASI V T

Jul 13, 2020

EXCELLENT

por Abhishek P G

Jun 15, 2020

satisfied