Introducing Image Classification through No-Code Teachable Machine Platform: An Intuitive Approach for Novice Learners

Abstract The technical complexity associated with traditional machine learning (ML) tools often impedes early-stage learners and non-programmers from exploring artificial intelligence (AI). This paper presents a pedagogically intuitive approach using Google’s Teachable Machine a no-code platform that allows users to create ML models via a graphical interface. We conducted an educational experiment where novice learners built an image classifier to distinguish between animal classes. Our experiment involved the classification of four animal class Cat, Dog, Monkey, and Tiger using a minimal dataset of just 3–5 images per class to ensure simplicity for beginners. Using transfer learning with MobileNet architecture and basic training settings (50 epochs, 0.001 learning rate), the model achieved high classification accuracy 100% for visually distinct classes and up to 97% for more similar ones. The platform’s ability to export trained models to TensorFlow, TensorFlow Lite, expands its potential for real-world deployment. This study concludes that GUI-based ML tools can democratize AI education and discuss their integration with IoT applications, making it accessible, interactive, and impactful for beginners.