A very nice project, although you need some understanding of the topic at hand before starting. Even though I did not know about deep learning before this project, I watched 3 Blue 1 Brown's first 2 videos about neural network, referred to some medium posts at Towards Data Science and read the Keras documentation when I did not understand something, and I found the project manageable.

It was fun to read and learn form this course. I am very happy with the infra that was provided in this course - it was very smooth experience.

Also, kudos to the instructor for having a very precise even pace all throughout and teaching a very useful thing, and i hope i will build further on it.

Thanks for the content, I browsed this course to know how the instructor would deal with class imbalance problem, but he didn't handle it, and dealt with the problem as if the classes were balanced.

Also I noticed when he made the validation generator, he enabled shuffling which I think is wrong as you don't want to shuffle the validation set and each epoch have a different subset of validation set to evaluate your model, you need to make to make sure that the validation set doesn't change per epoch to serve its purpose of had the model improved during this epoch?

All the concepts are well explained. The project gives a nice insight about how we can integrate different ML frameworks to build a project and also how to deploy the model as a web app by Flask.

This is a great hands-on project ! It is very well designed, and the instructor guides you to do it step by step. I enjoy this learning and practicing process a lot. Thank you !

Very easy to follow and the instructor was very informative throughout the project. As a beginner myself, it was easy for me to follow along and understand the project

This project gave me complete knowledge for implementiing the face recognition in future.This help me to built an app using FLASK.Its a good project to start with.

Great ressource to start practicing emotion recognition with famous domain's dataset FER. I also appreciate the using of web interface to display results.

Amazing start with having such types of the project by Coursera.

There is a lot to learn

This method of Teaching + Practical work simultaneously-----Amazing

Nice project! but the code in camera.py and the main.py file which is used to create a flask app to serve predictions should be explained in more detail.

Had fun exploring and optimizing the parameters. This project takes it to the next level while deploying to flask. Loved to work side by side.

The explanation provided by the mentor is really good! I like the way the project was compiled. Thank you so much for your time and efforts!

Good project to know the pipeline and simple deployment. however basic understanding of the machine learning terminology is needed.

Amazing Course as it provides learners, a facility of infrastructure as well as practise.

Great Experience, i learned a lot. !!!

Very Good for a person who is starting Machine Learning/Deep Learning. Seeing your project into action gives you motivation.

The course was so amazing.

I learned alot from this course and all things are really well-explained by our instructor.

Learned a lot of new things. Instructor also explained deeply every thing. Overall, a comprehensive course of FER.

A really good practical course if you'd like to learn how to implement a live Facial Recognition System.

I really learned a lot from this project. I would like to join in more project-based courses like this.

A really good course on how to apply theoretical knowledge into real world.

Course instructor was great!

It is great example for coding a convolutional neural network. The explanation is excellent.

Great Course. Highly recommend it to practice your machine learning skill and understanding.

Thank you for providing such a wonderful course.Enjoyed working on this project thoroughly.

It was a useful and practical demonstration of CNN application on human expressions. Kudos.

This project is good but I don't understand about to download the material what I have done