Udemy – Practical Computer Vision Mastery: 20+ Python & AI Projects 2025-6

Udemy – Practical Computer Vision Mastery: 20+ Python & AI Projects 2025-6 Downloadly IRSpace

Aug 17, 2025 - 02:15 Updated: Aug 19, 2025 - 14:12

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Udemy – Practical Computer Vision Mastery: 20+ Python & AI Projects 2025-6

Practical Computer Vision Mastery: 20+ Python & AI Projects. This course allows participants to learn computer vision skills and AI applications in image and video processing through 20+ real-world projects. Designed for students, STEM graduates, and professionals interested in AI, this course starts with the basics, such as installing Python, setting up VS Code, and working with OpenCV, and then moves on to more advanced techniques, such as edge detection, object tracking, OCR, and deep learning with TensorFlow/Keras and YOLO. Participants will work on projects as diverse as smart time and attendance, driver drowsiness detection, license plate recognition, safety monitoring with PPE detection, and vehicle speed tracking. By the end of the course, participants will be able to develop and optimize deep learning models, build graphical user interfaces, and implement industrial workflows. This course paves the way for entry or advancement into an AI career by providing a strong portfolio.

What you will learn

Understand the origins, evolution, and real-world impact of artificial intelligence, with a focus on the role of computer vision in modern applications.
Install and configure Python and VS Code to effortlessly develop vision-based projects on any platform.
Apply OpenCV basics: reading, writing, displaying, resizing, cropping, and converting the color space of images and videos.
Implementing image processing techniques such as thresholding, morphological transformations, bitwise operations, and histogram equalization.
Detect edges, corners, contours, and key points; match features in images to enable object recognition and scene analysis.
Use advanced methods such as Canny edge detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.
Building an intelligent face attendance system: registering faces, extracting embeddings, training a model, and setting up a Tkinter UI for live recognition.
Create a driver drowsiness tracker using EAR/MAR metrics, integrate it into a Tkinter dashboard, and perform real-time video inference.
Training YOLOv7-tiny for object and weapon detection, deploying it in Colab, and building a user interface for live detection.
Implement a YOLOv8 people counting and entry/exit tracker, visualize counts with Tkinter, and manage line coordinate logic.
Development of license plate recognition and recognition pipelines with Roboflow annotations, API integration, and live GUI display.
Building a traffic sign recognition system: data preprocessing, training EfficientNet-B0, and performing inference on the fly.
Building AI-based safety applications: accident detection with MQTT alerts, crash detection APIs, and intelligent vehicle speed tracking.
Detect emotions, age, and gender from live videos using pre-trained models and deploy via Tkinter interfaces.
Designing a real-time mask recognition program with YOLOv11, from dataset preparation to GUI inference.
Creating a hand gesture recognition system with keypoint annotation, MediaPipe state estimation, and interactive user interface.
Training a wildlife detection model on EfficientNetB0, deploying to Flask/Ngrok, and detecting animals in live streams.
OCR integration via Tesseract to extract text in images and build segmentation pipelines for robust scene analysis.

This course is suitable for people who:

Undergraduate and graduate students in engineering, computer science, electronics, or related fields who are looking for practical CV projects to complement their studies.
Recent graduates with STEM degrees who want to gain practical AI skills and showcase real-world projects on their resume.
Professionals working in software, electronics, robotics, or data roles who intend to enter AI/ML and use vision applications in industry.
Career changers are from STEM fields (such as physics, mathematics, biotechnology) and are looking for a structured path to enter computer vision without starting from scratch.
Research and Development (R&D) engineers and IoT developers who need to integrate vision analytics on edge devices like Jetson, Raspberry Pi, or in cloud pipelines.
Self-taught and enthusiasts with a scientific/engineering mindset who want to master the comprehensive CV workflow – from algorithm fundamentals to GUI deployment and model inference.

Course Details Practical Computer Vision Mastery: 20+ Python & AI Projects

Publisher: Udemy
Teacher: Muhammad Yaqoob G
Training level: Beginner to advanced
Training duration: 16 hours and 12 minutes
Number of courses: 244

Course syllabus in 2025/7

Prerequisites for the Practical Computer Vision Mastery: 20+ Python & AI Projects course

Basic Python programming knowledge
Windows PC or Laptop with 4GB+ RAM is recommended. A GPU is optional but helpful for faster model training and processing large datasets or real-time tasks.