⭐ Most Popular Course

Build a Real-Time Mask Detection

Design a real-time mask detection system using deep learning and OpenCV. Deploy on camera feeds to monitor mask compliance in public or workplace settings.
Beginner-friendly. Certificate included.

4.5/5 (1,000+ reviews)
2,000+ Students
8 Weeks
Zero to Pro Level
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 Face Recognition Technology

Explore how machines detect and recognize human faces in real time.

Python, OpenCV & Face Libraries

Use Python and OpenCV with powerful face detection libraries.

Live Face Detection & Recognition

Build a live system to identify faces from webcam video streams.

Database Integration & Attendance Logs

Store recognized faces and log attendance in a database system.

Graphical User Interface (GUI) with Tkinter

Design a user-friendly GUI to manage and view system actions.

Earn Your Certificate of Completion

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 to Face Mask Detection and Recognition
1min
Module 1 Video

Module 1

Introduction to Face Mask Detection and Recognition

Introduces the concept of detecting whether a person is wearing a face mask using computer vision, with applications in health safety and public surveillance.

Course Overview and Features

Real-Time Face Detection

Use Cases in Health and Safety

Integration with Real-Time Video

2
Environment setup for Python Development
3min
Module 1 Video

Module 2

Environment Setup for Python Development

Setting up Python and its development tools is the foundation of building AI applications. This ensures the environment is ready to run and develop object detection models.

Installing Python

VS Code Setup for Python Development

3
Face Mask Detection System Project Overview
2min
Module 1 Video

Module 3

Face Mask Detection Project Overview

Outlines the overall system architecture and workflow, explaining how each component—from dataset to model to interface—works together.

Face Mask Detection System Project Overview

Dataset Pipeline

Preprocessing & Augmentation

Real-Time Face Detection Integration

4
Google Drive Mount
1min
Module 1 Video

Module 4

Google Drive Mount

Explains how to mount Google Drive in Google Colab, enabling easy access to datasets and model files during training and evaluation.

Google Drive Mount

Need for Google Drive Integration

Navigating Drive Files

Accessing Datasets from Drive

Security & Privacy Tips

5
Face Mask Detection Dataset Download
1min
Module 1 Video

Module 5

Face Mask Detection Dataset Download

Walks through downloading a labeled dataset containing images of people with and without face masks, required for model training.

Face Mask Detection Dataset Download

Types of Face Mask Datasets

How to Download Public Datasets

Custom Dataset Collection Tips

Best Practices for Dataset Storage

6
Dataset Visualization
2min
Module 1 Video

Module 6

Dataset Visualization

Demonstrates how to visualize the dataset using Python tools to understand image distribution, mask categories, and data quality.

Dataset Visualization

Tools for Visualization in Python

Data Quality Check

7
Ultralytics Installation & Setting Up YOLOv11 for Mask Detection
2min
Module 1 Video

Module 7

Ultralytics Installation & Setting Up YOLOv11 for Mask Detection

Covers the installation of the Ultralytics YOLOv11 package and initial configuration for running object detection models for mask classification.

Ultralytics Installation & Setting Up YOLOv11 for Mask Detection

Analyzing feedback and identifying patterns

Iterating designs based on user insights

Creating comprehensive test documentation

Implementing accessibility testing protocols

Conducting A/B testing for design variations

Measuring and analyzing user engagement metrics

8
YOLOv11 Model Training for Mask Detection
6min
Module 11 Video

Module 8

YOLOv11 Model Training for Mask Detection

Explains how to train the YOLOv11 model using the dataset, adjusting parameters to optimize mask detection accuracy.

YOLOv11 Model Training for Mask Detection

Dataset Preparation (YOLO Format)

Validation and Accuracy Evaluation

9
Packages Explanation
4min
Module 1 Video

Module 9

Packages Explanation

Details the Python packages used in the project, such as OpenCV, YOLO, and matplotlib, and their specific roles in the detection pipeline.

Packages Explanation

Ultralytics YOLO (ultralytics) – Object Detection Framework

Torch / TensorFlow (Backend for YOLOv11)

10
Model Inference Code Explanation
8min
Module 1 Video

Module 10

Model Inference Code Explanation

Breaks down the inference script used to detect masks in images or video feeds, explaining how the trained model is applied in real-time.

Model Inference Code Explanation

Reading Input Sources (Image/Video/Camera)

Error Handling & Edge Cases

11
Tkinter Implementation
4min
Module 1 Video

Module 11

Tkinter Implementation

Teaches you how to create a basic GUI using Tkinter that allows users to run face mask detection through a simple and interactive interface.

Tkinter Implementation

Designing the GUI Layout

Integrating Model Inference with GUI

Final GUI Features Overview

12
Code Execution
6min
Module 1 Video

Module 12

Code Execution

Explains how to execute the complete code pipeline—from loading data and training the model to deploying and running inference on test inputs.

Code Execution

Environment Setup

Monitoring During Training

Deployment and Packaging

13
Wrapping Up
1min
Module 11 Video

Module 13

Wrapping Up

Summarizes what you’ve built, discusses potential improvements, and provides guidance on how to deploy or extend the system in real-world use cases.

Course Wrap-Up

Analyzing feedback and identifying patterns

Iterating designs based on user insights

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 recruiters 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

Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.

Key Values

Build Job-Ready Project Portfolios
Improve Debugging and Code Clarity
Experience Project-Based Learning
Boost Resume with Real Implementations

Pro Courses

₹499 Originally priced ₹999
Upgrade your learning with advanced content and mentor access

Highly recommended for small teams who seek to upgrade their time & perform.

PREMIUM DOWNLOADABLE RESOURCES
COURSE COMPLETION CERTIFICATE
MENTOR SUPPORT & PRIVATE COMMUNITY
LIFETIME UPDATES & PRIORITY SUPPORT

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Our Mentors

Muhammad Yaqoob

MUHAMMAD YAQOOB

Product Head
Pandian

PANDIAN

Senior AI Developer
Gowtham

GOWTHAM

Senior Edge AI Developer

Get Official Certified & Showcase Your Achievement 🔥

You can add this certificate in your Resume! Share it with your LinkedIn network 🚀

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Recognized by industry professionals worldwide
Perfect for LinkedIn profile and resume enhancement
Certificate

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.

Total Bonus Value: ₹20,000

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FAQ Section

What is covered in the Real-Time Mask Detection project?
You’ll build an AI model that identifies whether a person is wearing a face mask, using webcam feeds and YOLOv7 object detection.
Who is this course best suited for?
+
It’s designed for AI beginners, healthcare tech developers, and students interested in safety compliance AI systems.
Is real-time detection accuracy covered in the course?
+
Yes, the course explains how to improve detection accuracy and reduce false positives in real-time video input.
What tools are used in this mask detection project?
+
Python, OpenCV, YOLOv7, and dataset annotation tools are used to build and deploy the detection model.
Can this model be deployed on Raspberry Pi or embedded cameras?
+
Yes, it’s optimized for low-resource environments and can run on edge devices for on-the-go detection.
Does the course include code, dataset, and certification?
+
Absolutely. You get full access to the Python source code, labeled dataset links, training videos, and a verified course certificate.