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

99 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....

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1 - 17 of 17 Reviews for Dimensionality Reduction using an Autoencoder in Python

By Jorge G

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.

By Abhishek P G

Jun 15, 2020

I really enjoyed this class and the format it was presented in. For me, I learn and retain much more through an online class due to the fact you can do the course as an ¡§open book.¡¨ This really makes me search for the answer and in return, I retain more information. I found it relaxing to be able to turn the work on the assignments and test at my leisure and when I had the time. I liked the fact you were very clear that more internet research may be necessary for some assignments To be honest, there is nothing that I disliked about the course. I will definitely be taking another online course from you!

By Felix H

Jun 30, 2020

Nice project. Well explained, good duration. Main concept came across clearly.

By Ulvi I

May 4, 2020

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

By Ramya G R

Jun 13, 2020

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

By Mayank S

May 4, 2020

Nice Course, Well Explained, Thanks :)

By Oscar A C B

Jun 12, 2020

Nice example and great explanation.

By chandrasekhar u

May 6, 2020

Quite a new experience

By Gangone R

Jul 2, 2020

very useful course

By Doss D

Jul 2, 2020

Thank you

By Sarangan R

Jan 10, 2021


By Joerg A

May 19, 2020

The time is too short, especially if you want to not just type in the desktop, but also take notes. My huge problem was, that whenever I wanted to type in a different window, the video would stop. In the end I was kicked out about 5minutes when I would normally have finished.

The part about autoencoder, like which attributes (when doing print(autoencoder) are important could have been deeper.

I also learned some nice python and data science tricks. Hence the 4 stars and not 3. Also I guess the player constraints should not be accounted to the teacher.

By M H

Sep 17, 2020

Last two videos is really difficult for me, it will be very helpful if you please include some theories behind thode techniques in the reading section.

By Juan C V

Jul 5, 2020

Short and clear. A nice hand-ons introduction to the topic.

By Fabio S

Dec 25, 2023

very useful

By Sujeet B

May 7, 2020

1. The cloud time given was not enough. I thought we get ample time to do experiments and verify each step (and not just copy things over to the desktop). Disappointed to find, I was not allowed more "cloud time". Not sure, if that had something to do with the course fee paid (time limit based on fee amount?).

2. In the final set of questions: #6 was unrelated to the content covered in the project (Task 5). "Treating outliers as singletons was necessary to get a valid value for our Silhouette Scores". I am not sure when did we talk about 'outliers' and 'singletons' in Task 5. In the answers, Task #6 was referred. Was there a Task 6 dealt with, in the video? I couldn't find it in this project.

3. Did you miss "Does not" in Question #4 statement: "Which of these snippets express a ReLU function"?

All in all, it was a nice course. I do have an understanding of auto-encoders, compared with PCA now. I look forward to more such courses....

By Simon S R

Aug 29, 2020

Expected more from this project.