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Build a model that detects and classifies age and gender using deep learning. Use OpenCV and pretrained models for real-time predictions on video streams.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI identifies and analyzes facial features for recognition and classification.
Use Python with OpenCV and face libraries to build facial analysis applications.
Implement real-time face detection and age/gender prediction using a webcam.
Store recognized faces with timestamps and details in a connected database.
Create a user-friendly interface to manage detection and display results live.
Finish the course and receive a verified certificate of success.
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.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Introduces the objective of the project, showcasing how machine learning models can classify a person's age group and gender using facial recognition techniques.
Course Overview and Features
Applications in Real-World Scenarios
Facial Detection Fundamentals
Integration with GUI or Web Interface
Ethical Considerations & Bias Mitigation
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
Provides a breakdown of the complete project pipeline, from data input to model output, helping you understand the architecture and flow of the application.
Age and Gender Detection System Project Overview
Data Input and Acquisition
Face Detection Module
Performance Evaluation
System Optimization Techniques
Modular Design for Integration
Details the specific Python libraries and dependencies required to implement face detection, age classification, and gender prediction.
Packages Information
Dlib (Optional) – Facial Landmark Detection
Tkinter / Streamlit (Optional) – GUI Interface
OS / Glob / Shutil – File & Directory Handling
Covers how to define global variables and configure system parameters such as model paths, video input sources, and runtime flags.
Variable Initialization and System Configuration
Model & File Path Configuration
Scalability & Maintenance Focus
Shows how to organize project directories and create folders for storing models, output results, and logs to keep your project structured.
Folder Creation
Automatic Folder Creation with Python
Configuration Directory
Scalable for Larger Projects
Explains how to load pre-trained models and use them to perform real-time predictions for age and gender based on facial features from input images or webcam.
Model Inference
Face Detection Preprocessing
Accuracy and Confidence Thresholding
Optimized Runtime Handling
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recruiters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
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.
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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Get full project code for 20+ real-world applications – build, customize, and learn hands-on with working solutions.
Join weekly live Q&As to resolve queries and deepen your understanding with real-time support
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