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.
Learned Applying PCAConcise course.Liked the method of teaching.
Excellence experiece, good content for begineers, thanx coursera.
This is a great project. The instructor facilitates clear and practically.
It's clear for the new learner to follow up. Thank you.
Course is amazing, got many concepts clear, learned a lot. Would also be great if more than one datasets are taken as excercise.
It's a good course for someone to try out his knowledge of the basic packages and the concepts and the maths behind PCA.
It was quite conceptional but the instructor made it easy for me to implement and follow along.
Good Introductory project to gain insights into PCA using Numpy and python.
Instructor is amazing, explains the things very well
Good Exercise to practice and understand a little better.
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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.
Did not focus on the mathematics part of PCA. The explanation could have been better and easy to understand.
Muy buena explicación para cada uno de los aspectos del PCA.
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.
Well, this project seems to be very basic and can be created using WEBSITE LIKE:
https://www.geeksforgeeks.org/principal-component-analysis-with-python/
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.
Good Introductory project to gain insights into PCA using Numpy and python.
This is a great project. The instructor facilitates clear and practically.
Learned Applying PCA
Concise course.
Liked the method of teaching.
Good Exercise to practice and understand a little better.
It's clear for the new learner to follow up. Thank you.
Instructor is amazing, explains the things very well
simple and an elegant example to understand
Very good explanation with demo. Thank you.
This course is very useful in learning PCA
Nice and Helpful course...Thanks to Team
Thanks a lot Snehan .Learned a lot .
This is good course for beginners
It is a good project coursework.
Excellent course and instructor.