Practical Computer Vision with Python: OpenCV, MediaPipe, and YOLO

Computer vision is transforming industries from robotics to retail, yet getting started with complex image processing and neural networks can feel overwhelming. This course breaks down the core concepts of computer vision into clear, structured, text-based lessons that guide you from foundational math to advanced object detection. You will transition from writing basic image manipulation scripts to building complete computer vision pipelines. By understanding how computers interpret pixels, you will gain the confidence to apply modern deep learning models to real-world visual data. What you'll learn: Understand fundamental image representations, color spaces, and pixel-level operations using NumPy and OpenCV; Apply geometric transformations, filtering, and thresholding techniques to preprocess visual data; Implement augmented reality applications using ArUco markers for spatial tracking; Configure MediaPipe pipelines for real-time hand, face, and pose tracking; Train custom YOLO models on your own datasets for precise object detection; Practice writing clean, type-hinted Python code for robust and maintainable computer vision workflows. The journey begins with essential terminology, library setup, and basic image manipulation. From there, you will progress through classical computer vision techniques, explore augmented reality, and finally dive into state-of-the-art deep learning methods for object detection and pose estimation. This course is designed for beginner Python developers, aspiring data scientists, and software engineers who want to enter the field of computer vision. No prior experience with image processing or machine learning is required. Start reading today to unlock the power of visual data and build your first computer vision application.