Name: Principal Component Analysis with NumPy
Brand: Coursera
Availability: OnlineOnly
Rating: 4.6 (52 reviews)

Back to Principal Component Analysis with NumPy

Learner reviews & feedback for Principal Component Analysis with NumPy

4.6298 reviews

About this course

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.

Featured reviews

4.0Reviewed May 31, 2020

Course is amazing, got many concepts clear, learned a lot. Would also be great if more than one datasets are taken as excercise.

5.0Reviewed Apr 24, 2020

Learned Applying PCAConcise course.Liked the method of teaching.

4.0Reviewed Aug 4, 2020

It's a good course for someone to try out his knowledge of the basic packages and the concepts and the maths behind PCA.

5.0Reviewed Oct 30, 2020

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

4.0Reviewed May 31, 2020

It was quite conceptional but the instructor made it easy for me to implement and follow along.

4.0Reviewed May 8, 2020

Excellence experiece, good content for begineers, thanx coursera.

5.0Reviewed Nov 3, 2020

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

5.0Reviewed Sep 8, 2020

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

5.0Reviewed Jul 25, 2020

Good Exercise to practice and understand a little better.

5.0Reviewed Jul 17, 2020

Instructor is amazing, explains the things very well

All reviews

Showing: 20 of 52

Rishit Chaudhary

4.0

Reviewed Jun 1, 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.

Pranav Deshpande

3.0

Reviewed Jun 19, 2020

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

Karina Rodríguez Barragán

5.0

Reviewed Sep 10, 2020

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

Jorge García

3.0

Reviewed Feb 25, 2021

I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material. Of course coursera gives me a small discount for having already paid it previously. It is very easy to download the videos and difficult to get hold of the material, but with ingenuity it is possible. Then I recommend uploading them to YouTube and keeping them private for when they want to consult (they avoid legal problems and can share with friends), then they can request a refund.

Mohinder Sharma

3.0

Reviewed Jun 3, 2020

Well, this project seems to be very basic and can be created using WEBSITE LIKE:

https://www.geeksforgeeks.org/principal-component-analysis-with-python/

Zixiang Ma

2.0

Reviewed Jun 11, 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.

Tanuj Agrawal

5.0

Reviewed Oct 31, 2020

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

Hector Perez

5.0

Reviewed Sep 9, 2020

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

Mayank Singhal

5.0

Reviewed Apr 24, 2020

Learned Applying PCA

Concise course.

Liked the method of teaching.

Jose Alvarez

5.0

Reviewed Jul 26, 2020

Good Exercise to practice and understand a little better.

LIN FAN

5.0

Reviewed Nov 4, 2020

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

VIJAY KUMAR

5.0

Reviewed Jul 18, 2020

Instructor is amazing, explains the things very well

Dr.T.Hemalatha cse

5.0

Reviewed Jun 9, 2020

simple and an elegant example to understand

Jayasanthi

5.0

Reviewed Apr 25, 2020

Very good explanation with demo. Thank you.

Dr. Chandra Shekara G

5.0

Reviewed Jun 9, 2020

This course is very useful in learning PCA

Punam Patil

5.0

Reviewed May 12, 2020

Nice and Helpful course...Thanks to Team

Prajwal K V

5.0

Reviewed Nov 11, 2020

Thanks a lot Snehan .Learned a lot .

Dr. Pravin Wararkar

5.0

Reviewed May 31, 2020

This is good course for beginners

math tutorials (Math Teacher)

5.0

Reviewed Dec 9, 2023

It is a good project coursework.

Syed Ahsan Raza

5.0

Reviewed Nov 3, 2020

Excellent course and instructor.