Mask Detection with Python and TensorFlow: Build and Deploy

Computer vision is transforming how we interact with the world, making image analysis a highly sought-after skill in modern software development. Understanding how to process images and train deep learning models is the first step toward building intelligent visual applications. By completing this text-only course, you will understand the foundational principles of computer vision and deep learning. You will learn how to prepare image data, construct a neural network using TensorFlow, and construct a system that accurately detects face masks in images. What you'll learn: - Understand the foundational concepts of computer vision, image processing, and neural networks. - Apply OpenCV techniques to load, resize, and pre-process image datasets for training. - Build and train a deep learning classification model using modern TensorFlow and Keras APIs. - Evaluate model performance using key metrics to ensure high accuracy. - Deploy your trained model using a lightweight Python interface for practical application. - Practice writing clean, modern Python code using virtual environments and structured project layouts. The course begins with core terminology and image processing basics before moving step-by-step through dataset preparation, model training, and deployment. You will work through clear written explanations and structured code snippets designed to build your confidence. This course is designed for beginners interested in computer vision and machine learning, with no prior experience in TensorFlow or OpenCV required. Start reading today to build your first practical deep learning application from the ground up.