Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
15 Hour(s) 43 Minute(s)
Language
English
Taught by
Muhammad Yaqoob G
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Master Computer Vision Course in 2025 with Deep Learning, Python, OpenCV, YOLO, OCR & GUI through 20+ handson projects
Unlock the power of image- and video-based AI in 2025 with 20+ real-time projects that guide you from foundational theory to fully functional applications. Designed for engineering and science students, STEM graduates, and professionals switching into AI, this hands-on course equips you with end-to-end computer vision skills to build a standout portfolio.
Key Highlights:
Environment Setup & Basics: Install Python, configure VS Code, and master OpenCV operations—image I/O, color spaces, resizing, thresholding, filters, morphology, bitwise ops, and histogram equalization.
Core & Advanced Techniques: Implement edge detection (Sobel, Canny), contour/corner/keypoint detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.
Deep Learning Integration: Train and deploy TensorFlow/Keras models (EfficientNet-B0) alongside YOLOv7-tiny and YOLOv8 for robust detection tasks.
GUI Development: Build interactive Tkinter interfaces to visualize live video feeds, detection results, and system dashboards.
20+ Hands-On Projects Include:
Smart Face Attendance with face enrollment, embedding extraction, model training, and GUI integration.
Driver Drowsiness Detection using EAR/MAR algorithms and real-time alert dashboards.
YOLO Object & Weapon Detection pipelines for live inference and visualization.
People Counting & Entry/Exit Tracking with configurable line-coordinate logic.
License-Plate & Traffic Sign Recognition leveraging Roboflow annotations and custom model training.
Intrusion & PPE Detection for workplace safety monitoring.
Accident & Fall Detection with MQTT alert systems.
Mask, Emotion, Age/Gender & Hand-Gesture Recognition using custom-trained vision models.
Wildlife Identification with EfficientNet-based classification in live streams.
Vehicle Speed Tracking using calibration and object motion analysis.
By course end, you’ll be able to:
Develop, train, and fine-tune deep-learning vision models for diverse real-world tasks.
Integrate CV pipelines into intuitive GUIs for live video applications.
Execute industry-standard workflows: data annotation, training, evaluation, and deployment.
Showcase a portfolio of 20+ complete projects to launch or advance your AI career.
Enroll today and start building your first real-time computer vision app!
Course Content
- 30 section(s)
- 245 lecture(s)
- Section 1 Introduction
- Section 2 Meet Your Instructor
- Section 3 Expected Output Preview – See What You’ll Build
- Section 4 Understanding AI – From Origins to Impact
- Section 5 Overview of computer vision
- Section 6 Environment Setup for Python Development
- Section 7 Computer Vision Basic Techniques
- Section 8 Computer Vision Advanced Techniques
- Section 9 Project #1: Smart Face Attendance System with Python & Computer Vision
- Section 10 Project #2: Driver Drowsiness Detection System with Python & Computer Vision
- Section 11 Project #3: Object Detection Using Yolov7 with Python & Computer Vision
- Section 12 Project #4: AI-Powered Weapon Detection for Enhanced Security with Python & CV
- Section 13 Project #5: Real-Time Entry/Exit Occupancy Tracker using Python & OpenCV
- Section 14 Project #6: Facial Emotion Detection & Recognition using Python & OpenCV
- Section 15 Project #7: LLM-Powered License Plate Detection & Recognition with Python & CV
- Section 16 Project #8: Driving with AI – Real-Time Traffic Sign Detection with Python & CV
- Section 17 Project #9: Smart Human Intrusion Detection System with Python & Computer Vision
- Section 18 Project #10: AI-Powered PPE Detection: Ensuring Workplace Safety in Real Time
- Section 19 Project #11: AI Vision – Age & Gender Detection with Python & Computer Vision
- Section 20 Project #12: AI Accident Detection & Real-Time Monitoring with Python & CV
- Section 21 Project #13: Smart Vehicle Speed Tracking System with Python & Computer Vision
- Section 22 Project #14: Real-Time Vehicle Parking Management with Python & Computer Vision
- Section 23 Project #15: Real-Time Mask Detection with AI using Python & Computer Vision
- Section 24 Project #16: Real-Time Hand Gesture Detection & Recognition with Python & CV
- Section 25 Project #17: Smart Vehicle Traffic Monitoring System with Python & CV
- Section 26 Project #18: Smart Fitness – Real-Time Exercise Counter using AI, Python & CV
- Section 27 Project #19: SafeFall – AI-Powered Fall Detection & Alerts with Python & CV
- Section 28 Project #20: Wildlife Tracking – Real-Time Animal ID with Python & CV
- Section 29 Project #21: AI-Powered Driver Monitoring – Distraction Detection with Python
- Section 30 Wrapping Up
What You’ll Learn
- Understand the origins, evolution, and real-world impact of AI, with a focus on computer vision’s role in modern applications.
- Install and configure Python and VS Code for seamless development of vision-based projects on any platform.
- Apply OpenCV fundamentals—reading, writing, displaying, resizing, cropping, and color-space conversion of images and videos.
- Implement image processing techniques such as thresholding, morphological transforms, bitwise operations, and histogram equalization.
- Detect edges, corners, contours, and keypoints
- match features across images to enable object recognition and scene analysis.
- Leverage advanced methods—Canny edge detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.
- Build a smart face‐attendance system: enroll faces, extract embeddings, train a model, and launch a Tkinter GUI for live recognition.
- Create a driver-drowsiness detector using EAR/MAR metrics, integrate it into a Tkinter dashboard, and run real-time video inference.
- Train YOLOv7-tiny for object and weapon detection, deploy in Colab, and build a GUI for live detection.
- Implement a YOLOv8 people‐counting and entry/exit tracker, visualize counts with Tkinter, and manage line‐coordinate logic.
- Develop license‐plate detection & recognition pipelines with Roboflow annotations, API integration, and live GUI display.
- Craft a traffic‐sign recognition system: preprocess data, train EfficientNet-B0, and perform inference in real time.
- Build AI-powered safety apps: accident detection with MQTT alerts, fall-detection APIs, and smart vehicle speed tracking.
- Detect emotions, age, and gender from live video using pre-trained models and deploy via Tkinter interfaces.
- Design a real-time mask detection application with YOLOv11, from dataset prep to GUI inference.
- Create a hand-gesture recognition system with landmark annotation, MediaPipe pose estimation, and interactive GUI.
- Train a wildlife identification model on EfficientNetB0, deploy in Flask/Ngrok, and recognize animals in live streams.
- Integrate OCR via Tesseract for text extraction in images and build segmentation pipelines for robust scene parsing.
Skills covered in this course
Reviews
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AAbu Hasan
legit Teaching is very good. even if you are a beginner in Computer Vision concepts, this course covers everything from scratch. It will be helpful to understan
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MMohamed Mirzan M
Really an amazing course,Really I don't know how this guyz providing this course with this cost, They taught lot of projects with very understandable way , with source code too, I strated this course 2 days ago so far the course content and teaching is excellent.
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KKrishna Murthy
Very well explained
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PPierre S
It's really a amazing course specially which is beginner and want to learn open cv concepts. You will get interactive coding with a lot of projects. And the first instructor part is a art.
