This complete guide takes you step by step through the process of combining Flutter’s cross-platform power with machine learning models for Android and iOS. You’ll start by setting up new Flutter projects, adding the necessary libraries and enabling image selection from the gallery or camera. Lessons also cover displaying live camera footage and building real-time feed applications to prepare your apps for on-device ML tasks.

You’ll then dive into practical machine learning implementations. Modules cover image labelling and classification, barcode scanning, face detection, object detection and text recognition, with each topic taught first on static images and then in real-time using live camera streams. You’ll learn how to draw bounding boxes, facial contours and landmarks, classify objects and text, and handle testing and performance considerations for real-time apps.

Beyond vision tasks, the course teaches you how to add language-related features like text translation, language identification and smart reply generation, as well as entity extraction and digital ink (handwriting) recognition. You’ll also work with pre-trained models for image classification and object detection, integrate TensorFlow Hub models, and implement MobileNet and EfficientNet architectures for both images and objects.

Advanced sections cover training your own machine learning models using Google Colab, uploading and testing data, retraining MobileNet, ResNet and other models, deploying them into Flutter projects and optimising real-time recognition. By the end of the course, you’ll know how to combine Flutter with machine learning to create production-ready apps that recognise images, text and objects seamlessly across devices.