Introduction to AI with Teachable Machine

In this lesson, you will learn how to define Artificial Intelligence (AI) and Machine Learning (ML), identify real-world uses of AI and ML, explain the role of data in AI, recognize ethical considerations in AI, and develop interest in exploring AI further.

In this lesson, you will learn how computers can see and recognize objects, respond to gestures, react to different sounds, and understand the role of AI in making these interactions possible, and now you can get hands-on experience like a real AI engineer using Google Teachable Machine, with no programming required!

In this lesson, you will learn the importance of data in training AI models, how to identify and categorize different types of data such as images, sounds, and text, how to collect and prepare data for a chosen project, analyze the quality of data and its impact on AI model performance, and get ready to become an AI creator.

In this lesson, you will learn the steps involved in creating, training, and testing an AI model, apply techniques to train and test AI models using a dataset, iterate on models to improve performance based on testing results, evaluate model performance including recognizing overfitting, underfitting, and accuracy issues, and get ready to become an AI creator.

In this lesson, you will learn how to analyze weaknesses in an AI model and identify areas for improvement, apply techniques such as data augmentation and parameter adjustment to enhance model performance, evaluate how model complexity affects performance and accuracy, and create an improved version of your model while documenting the enhancement process.

In this lesson, you will learn to understand the ethical implications of AI, including issues like bias, privacy, and accountability, analyze real-world case studies to identify ethical challenges in AI systems, evaluate AI systems critically from an ethical perspective, and reflect on and explain strategies to ensure ethical AI use in your own projects.

In this lesson, you will learn to recall the key Sustainable Development Goals (SDGs) and understand their global importance, identify how AI technologies can contribute to achieving specific SDGs, brainstorm AI-based solutions that align with a chosen SDG, and develop an initial plan for a final project that uses AI to address an SDG, including data requirements.

In this lesson, you will learn the key steps involved in designing an AI project, including setting objectives, collecting data, and designing the model, apply planning techniques to create a structured AI project plan, develop a comprehensive plan with clear goals, timelines, milestones, and resources, and evaluate your own and your peers’ project plans for clarity, feasibility, and effectiveness.

In this lesson, you will learn how to transition from project planning to execution by setting up the environment, coding, and training the model, apply your knowledge to develop the AI project according to your plan, analyze and troubleshoot common issues during AI development using problem-solving techniques, and document your progress, including challenges and solutions.

In this lesson, you will learn the steps required to transition from project planning to execution, including setting up the environment, coding, and training the model, apply coding and implementation techniques to develop your AI projects according to your plans, analyze and troubleshoot common issues during development, and document your progress, including challenges faced and solutions implemented.

In this lesson, you will learn to communicate your AI projects effectively to diverse audiences, structure a clear, engaging, and informative project presentation, and develop peer-review skills through giving and receiving constructive feedback on presentations.

In this lesson, you will have the opportunity to showcase your AI projects, reflect on your learning journey, and evaluate your experiences, challenges, and future plans in AI.