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.

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.

The couse was made very simple.

Corso davvero utile e semplice.

Just as simple as I needed!

Great course for beginners

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