Face Mask Detection with TensorFlow and OpenCV

Computer vision is transforming how we interact with public spaces and ensure safety. Building a real-time object classification system is one of the most practical ways to master deep learning. In this text-based course, you will learn how to design, train, and implement a convolutional neural network (CNN) that automatically detects whether individuals are wearing face masks. You will start with the fundamental concepts of image preprocessing and computer vision before moving on to practical implementation using TensorFlow and OpenCV. What you'll learn: - Understand the core concepts of computer vision, image representation, and deep learning architectures. - Preprocess raw image datasets using Python libraries to prepare them for neural network training. - Build a Convolutional Neural Network (CNN) from scratch to classify masked and unmasked faces. - Apply transfer learning using modern, lightweight architectures optimized for real-time edge devices. - Implement face detection on static images and live video streams using OpenCV. - Evaluate your model's performance using confusion matrices, precision, recall, and F1-score. The course begins with foundational concepts in image processing and neural network design. You will then progress through data preparation, model training, and real-time inference implementation using standard computer vision pipelines. This course is designed for beginners in data science and machine learning who want to build a practical computer vision portfolio project. No prior experience with deep learning is required, though basic Python familiarity is helpful. Start reading today to build your first real-world computer vision application.