Abstract

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and robotics. In this paper, we present an overview of AI Trained object recognition using Google Teachable Machine. Google Teachable Machine is a web-based platform that allows users to train machine learning models without any coding or programming skills. We explore the steps involved in training an object recognition model using Google Teachable Machine and evaluate the performance of the model on a real-world dataset. Our results show that Google Teachable Machine is a powerful and user-friendly tool for training object recognition models with high accuracy. This research focuses on the development of an AI-ML trained object recognition system using Google Teachable Machine, enhanced by data science methodologies. The objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in real-time. Leveraging the user-friendly interface of Google Teachable Machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing techniques. This approach ensures high accuracy and reliability in object recognition tasks. The integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and adaptability. The resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems.