Practical Computer Vision Mastery: 20+ Python & AI Projects
Master Computer Vision Course in 2025 with Deep Learning, Python, OpenCV, YOLO, OCR & GUI through 20+ handson projects
Practical Computer Vision Mastery: 20+ Python & AI Projects free download
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!
