Practical Computer Vision Mastery: 20+ Python & AI Projects | Udemy [Update 06/2025]
English | Size: 12.6 GB
Genre: eLearning[/center]

Master Computer Vision Course in 2025 with Deep Learning, Python, OpenCV, YOLO, OCR & GUI through 20+ handson projects

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.

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!

Who this course is for:
Undergraduate and Graduate Students in engineering, computer science, electronics or related fields seeking hands-on CV projects to complement their studies.
Recent Graduates with STEM degrees who want to build practical AI skills and showcase real-world projects on their résumé.
Working Professionals in software, electronics, robotics or data roles aiming to pivot into AI/ML and leverage vision applications in industry.
Career-Switchers from STEM Fields (e.g., physics, mathematics, biotech) looking for a structured path into computer vision without starting from scratch.
R&D Engineers & IoT Developers who need to integrate vision analytics on edge devices like Jetson, Raspberry Pi or in cloud pipelines.
Self-Learners & Hobbyists with a science/engineering mindset who want to master end-to-end CV workflows-from algorithm basics to GUI deployment and model inference.

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