Mask Detector with Python & TensorFlow: Build & Deploy
About this course
Build a real-world mask detection application with Python, OpenCV, TensorFlow, and AWS. In this hands-on, project-based course, you’ll learn the complete computer vision workflow, beginning with reading, writing, resizing, cropping, and annotating images. You’ll then use Haar Cascade classifiers to analyze facial features and develop the TensorFlow foundations needed for deep learning. As you progress, you’ll build and train a Convolutional Neural Network with MobileNetV2 to classify masked and unmasked faces. You’ll also design an interactive front-end interface, add file-upload functionality, and integrate your trained model into a working mask detection app. Finally, you’ll deploy, test, and validate the application on AWS for scalable, real-world use. Designed for aspiring data scientists, AI enthusiasts, and developers, this course takes you beyond theory through an end-to-end AI application project. Its concept-to-cloud approach connects image preprocessing, face detection, deep learning model training, app development, and cloud deployment in one practical learning experience. Enroll to strengthen your computer vision and TensorFlow skills while creating a deployable project for your portfolio.
75/100
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- 39/45
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- 20/35
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What you'll learn
- analyze images with OpenCV
- build and train deep learning models with TensorFlow
- design interactive applications
- deploy AI solutions on AWS
- implement face detection techniques using Haar Cascades
- explore deep learning with Convolutional Neural Networks
Course objectives
- provide a step-by-step guide from data preprocessing to application deployment
- help learners gain both technical knowledge and deployment skills
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