Chevron Left
Back to Dimensionality Reduction using an Autoencoder in Python

Learner Reviews & Feedback for Dimensionality Reduction using an Autoencoder in Python by Coursera Project Network

4.6
stars
94 ratings

About the Course

In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Top reviews

UI

May 3, 2020

Very practical and useful introductory course. Looking for the next courses :)

RR

Jun 12, 2020

I really enjoyed this course. Thank you very much for the valuable teaching.

Filter by:

1 - 16 of 16 Reviews for Dimensionality Reduction using an Autoencoder in Python

By Abhishek P G

Jun 15, 2020

By Felix H

Jun 30, 2020

By Ulvi I

May 4, 2020

By Ramya G R

Jun 13, 2020

By Mayank S

May 4, 2020

By Oscar A C B

Jun 12, 2020

By chandrasekhar u

May 6, 2020

By Gangone R

Jul 2, 2020

By Doss D

Jul 2, 2020

By Sarangan R

Jan 10, 2021

By Joerg A

May 19, 2020

By M H

Sep 17, 2020

By Juan C V

Jul 5, 2020

By Sujeet B

May 7, 2020

By Jorge G

Feb 25, 2021

By Simon S R

Aug 29, 2020