Facial Expression Recognition with Keras from Coursera

In this 2-hour long project-based course, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. 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, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

Good to know

Know what's good

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Teaches skills and knowledge highly relevant to the field of image processing and facial recognition

Offers hands-on labs and interactive materials to reinforce learning

Requires a basic understanding of OpenCV and Python

Provides a strong foundation for beginners in the field of facial expression recognition

Will take place on a hands-on project platform, which may not be suitable for all learners

Reviews summary

Hands-on facial recognition project

This beginner-friendly course is a hands-on project that will teach you how to implement a live Facial Expression Recognition System using Keras. It is a great starting point for understanding how to put theoretical knowledge into real-world applications.

This course is suitable for beginners, but may be challenging for those with no programming or Python experience.

"perfect course for beginners. step by step explanation makes it easier for learners."

"not for someone with no background in programming/python etc. They should have mentioned this! Disappointed"

This course provides practical knowledge and skills in implementing a facial recognition system.

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

This course was enjoyable and beneficial learning experience.

"I really enjoyed this Project ..."

"Very exciting project and instructor is also very good,and explained very well everything."

The instructor provides clear explanations and guidance throughout the course.

"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!"

"The course was so amazing.I learned alot from this course and all things are really well-explained by our instructor."

Rhyme, the hands-on project platform, has some limitations and can be frustrating to use.

"The majority of the questions are not answered. "

"Rhyme software is too much slow and it is very boaring to work on that and also font is too much small."

"The rhyme interface kept wasting time by freezing either the videos or the cloud computer"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Facial Expression Recognition with Keras with these activities:

Sharpen Your Python Skills

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Ensuring proficiency in Python will enhance your ability to apply the techniques covered in this course.

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Review Python syntax and data structures
Practice writing Python code through online exercises or tutorials
Contribute to open-source Python projects

Review Convolutional Neural Networks (CNNs)

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Refreshing your knowledge of CNNs will provide a strong foundation for this course.

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Review lecture notes from previous courses or online resources
Solve practice problems related to CNNs
Participate in online discussions or forums related to CNNs

Identify Mentors in the Field

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Connecting with experienced professionals will provide guidance and support throughout your learning journey.

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Research and identify potential mentors
Reach out to mentors and schedule introductory meetings
Establish a regular communication plan

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General Chemistry Textbook Review

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Review the General Chemistry textbook to strengthen foundational knowledge of chemical principles.

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Read through each chapter
Take notes on key concepts
Complete practice problems at the end of each chapter

Join Study Groups with Classmates

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Participating in study groups will enhance your understanding through peer discussions and problem-solving.

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Find classmates with similar learning goals
Establish regular meeting times and study plans
Collaborate on assignments, discuss concepts, and solve problems together

Python Coding Practice Drills

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Regular practice with coding drills will reinforce your understanding of Python fundamentals.

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Solve coding challenges on platforms like LeetCode or HackerRank
Participate in online coding competitions

Attend Machine Learning Workshops

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Workshops will provide hands-on experience and expose you to practical applications of machine learning.

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Search for upcoming machine learning workshops
Register and attend the workshops
Participate actively and ask questions

Create a Machine Learning Glossary

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Creating a glossary of machine learning terms will deepen your understanding of key concepts and their relationships.

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Identify and define essential machine learning terms
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Write clear and concise definitions

Career center

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