In an era of automations, this course introduces kids to AI and machine learning through fun, hands-on projects. Using tools like Scratch, Teachable Machine, and beginner-friendly Python, they’ll explore how machines recognize patterns, make decisions, and learn from data. Key concepts include training models, data classification, and AI approaches like supervised learning and rule-based systems.
This course introduces kids to the fundamental concepts of artificial intelligence and machine learning through interactive, beginner-friendly tools and projects. Students learn how machines can be trained to recognize patterns, make predictions, and respond intelligently to data. Through experimentation and creative problem-solving, they will explore how different AI techniques are used in real-world applications like image recognition, game AI, and smart assistants.
Students will build simple AI models using visual and code-based platforms to understand how data is collected, processed, and used to make decisions. They’ll also learn that different AI approaches are suited to different types of problems, and develop the ability to choose appropriate tools and methods depending on the task at hand.
• Scratch – A visual programming platform that helps kids understand logic and flow control. In this course, it’s used to design intelligent games and projects with event-driven and rule-based behaviors.
• Python – A widely-used, beginner-friendly programming language that forms the basis for many AI and ML applications. Students use it for building simple classifiers and training basic models using intuitive libraries.
• Teachable Machine – A web-based, no-code tool developed by Google that allows kids to train AI models using their own data, such as images or sounds, to explore supervised learning and classification.
• Machine Learning Concepts – The course introduces supervised learning (learning from labeled data), rule-based systems (using logic and conditions to make decisions), and simple reinforcement learning ideas (learning by trial and error), giving students a broad understanding of AI paradigms.