⭐ Most Popular Course

Build a Facial Emotion Detection

Build a facial emotion recognition system using Python, OpenCV, and deep learning models. Detect emotions like happy, sad, angry, and surprise from facial expressions.
Beginner-friendly. Certificate included.

4.9/5 (1,000+ reviews)
2,000+ Students
8 Weeks
Zero to Pro Lvl
Certificate

Our Instructors Collaborate With Top Tech Leaders

Google Startups
AWS
Microsoft Microsoft
NVIDIA NVIDIA

What You'll Learn in this course

By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.

Introduction to Emotion Recognition Technology

Learn how AI analyzes facial expressions to detect human emotions in real time.

Python, OpenCV & Deep Learning Emotion Models

Use Python with OpenCV and pre-trained CNN models to classify emotions like happy, sad, angry, and more.

Live Emotion Detection via Camera Feed

Build a real-time emotion recognition system using a webcam to analyze user expressions dynamically.

Database Integration & Emotion Logs

Store detected emotions with timestamps in a database for emotional behavior tracking and reports.

Graphical User Interface (GUI) with Tkinter

Design an engaging GUI that displays the video feed and detected emotions in real time..

Earn Your Course Completion Certificate

Finish the course and receive a verified certificate of success.

Module 1 Video

Meet Your Instructor

Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.

Course Curriculum

8 weeks of comprehensive training with 50+ lessons and 10+ hours of content

1
Introduction of the Facial Emotion Detection
2min
Module 1 Video

Module 1

Introduction of the Facial Emotion Detection

Facial Emotion Detection is a computer vision technology that identifies human emotions from facial expressions using AI. It plays a vital role in fields like healthcare, marketing, and human-computer interaction.

Course Introduction and Features

Detect emotions from faces

Real-time emotion tracking

2
Environment Setup for Python Development
3min
Module 1 Video

Module 2

Environment Setup for Python Development

Setting up the Python environment involves installing Python, essential libraries like OpenCV and TensorFlow, and a code editor. This foundation allows smooth development and testing of AI-based computer vision applications.

Installing Python

VS Code Setup for Python Development

3
Facial Emotion Detection Project Overview
1min
Module 1 Video

Module 3

Facial Emotion Detection Project Overview

This project involves detecting human emotions using facial expressions through machine learning and deep learning models. It combines datasets, model training, and inference to classify emotions like happy, sad, angry, or neutral.

Facial Emotion Detection Project Overview

Detect emotions via face

Open Colab in browser

4
Set up Google Colab
1min
Module 1 Video

Module 4

Set up Google Colab

Google Colab is a free cloud-based Jupyter notebook environment. Setting it up allows running Python code with GPU support, enabling faster development and testing of AI and deep learning models.

Set up Google Colab

Open Colab in browser

Fast model development

5
Facial Emotion Detection Dataset Download
2min
Module 1 Video

Module 5

Facial Emotion Detection Dataset Download

The dataset consists of facial images labeled with emotions. Downloading and preprocessing this dataset is the first step in training a model to classify facial expressions accurately using deep learning techniques.

Facial Emotion Detection Dataset Download

Download facial image data

Essential for emotion tasks

6
Dataset Visualization
3min
Module 1 Video

Module 6

Dataset Visualization

Dataset Visualization focuses on representing the distribution and characteristics of the facial emotion dataset using visual tools. Techniques like emotion count bar charts, sample image grids, and class balance plots help in understanding data quality, class imbalance, and sample diversity before training the model.

Dataset Visualization

Plot emotion class counts

Prep for better training

7
Pre-trained yolov9 model weight file download
2min
Module 1 Video

Module 7

Pre-trained yolov9 model weight file download

Pre-trained weights for YOLOv9 help in initializing the model with learned features, significantly improving training speed and accuracy. These weights are downloaded before beginning the fine-tuning or inference process.

Pre-trained yolov9 model weight file download

Use pretrained model

Needed for inference tasks

8
YOLOv9 Model info
3min
Module 1 Video

Module 8

yolov9 model info

YOLOv9 (You Only Look Once version 9) is a cutting-edge real-time object detection model. It’s known for high speed and accuracy, making it suitable for detecting faces and emotions in live video feeds.

yolov9 model info

Latest YOLO version

Ideal for emotion tracking

Efficient and powerful

9
YOLOv9 model code explanation
15min
Module 1 Video

Module 9

yolov9 model code explanation

The YOLOv9 model code is structured to include input preprocessing, model architecture definition, training loops, and loss functions. Each component works together to accurately detect and classify facial features and emotions.

yolov9 model code explanation

Preprocess input images

Detect facial features

Supports real-time tasks

10
YOLOv9 model training
5min
Module 1 Video

Module 10

yolov9 model training

Model training involves feeding the dataset into YOLOv9, adjusting weights using backpropagation, and optimizing for accuracy. This phase is essential for teaching the model to recognize various facial emotions effectively.

yolov9 model training

Feed data to YOLOv9

Monitor loss and metrics

Core learning phase

11
Model Inference Explanation
12min
Module 10 Video

Module 11

Model Inference Explanation

Inference is the process of using a trained model to predict emotions from new facial images. It involves image preprocessing, model loading, and generating output labels representing different emotional states.

Model Inference Code Explanation

Load trained model

Preprocess input image

12
Code Execution
5min
Module 11 Video

Module 12

Code Execution

Running the complete code pipeline includes loading the model, processing input data, and displaying predictions. This execution verifies the functionality of the trained model and demonstrates its emotion detection performance.

Code Execution

Analyzing feedback and identifying patterns

Iterating designs based on user insights

13
Course Wrap-Up
1min
Module 12 Video

Module 13

Course Wrap-Up

The project summary reviews key steps such as dataset preparation, model setup, training, and inference. It also suggests possible improvements and applications of facial emotion detection in real-world scenarios.

Who This Course Is For

Is This Course Right for You?

AI Enthusiasts

Kickstart your AI journey with structured, hands-on learning.

Students & Freshers

Build a portfolio that recuriters can't ignore.

Developers

Add powerful AI/CV features to your apps and software.

Working Professionals

Upskill for higher-paying, future-ready tech roles.

Freelancers & Founders

Build Smarter, more intelligent applications.

Career Switchers

Transition into AI even with zero background.

Simple, Transparent Pricing

One-time payment for lifetime access to all course materials and updates

Learn With Our best mentors

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key Value

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Our Mentors

Muhammad Yaqoob

MUHAMMAD YAQOOB

Product Head
Pandian

PANDIAN

Senior AI Developer
Gowtham

GOWTHAM

Senior Gen AI Developer

Get Official Certified & Showcase Your Achievement 🔥

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Certificate

OF ACHIEVEMENT

This Certificate Is Proudly Presented To

Jacqueline Thompson

This certificate is given to Jacqueline Thompson for her achievement in the field of education and proves that she is competent in her field.

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Exclusive Free Bonuses Included With Your Course

Downloadable Source Code

Get full project code for 20+ real-world applications – build, customize, and learn hands-on with working solutions.

Live Doubt-Clearing Sessions

Join weekly live Q&As to resolve queries and deepen your understanding with real-time support

Soft Skills & Career Growth Kit

Enhance your confidence with communication tips, resume builder templates, and personal branding guides tailored for tech careers.

Private Learners Community

Get feedback, share wins, and grow with other learners in a safe and supportive environment.

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FAQ Section

Is this course suitable for beginners?
Yes! No prior AI or computer vision experience is required.
Will I receive a certificate?
+
Yes. A verified certificate is awarded upon course completion.
Do I need to know Python already?
+
Basic understanding helps, but we cover what you need inside the course.
Are the projects job-ready?
+
Absolutely! You’ll build 20+ practical projects useful in real-life.
Can I access the course offline?
+
Yes, all content can be downloaded for offline use.
How long will I have access to the course?
+
You have lifetime access to the course materials.