Description
🔹 Course Vision
This course is designed for students who already have a basic foundation in coding and data analytics (Python, Tableau, Excel, etc.) and are ready to take the next step into the world of artificial intelligence and machine learning. Through hands-on projects, simplified theory, and real-world applications, students will learn how AI works, where it’s used, and how they can begin building their own intelligent systems.
We focus not only on tools, but on ethical awareness, problem-solving mindset, and cross-disciplinary integration, helping students become thoughtful, future-ready creators in the AI era.
🔹 What Students Will Learn
1. Introduction to Artificial Intelligence
- What is AI?
- History and types of AI
- Real-world examples in daily life and industry
2. Python for AI
- Review of Python basics
- Data handling with Pandas & NumPy
- Introduction to Scikit-learn (sklearn)
3. Machine Learning Fundamentals
- Supervised vs. Unsupervised Learning
- Key concepts: training, testing, overfitting
- Simple algorithms: Linear Regression, Decision Trees, K-Means
4. Data Preparation & Visualization
- Cleaning and preprocessing data
- Visualizing patterns and results with Matplotlib & Seaborn
5. Building Simple AI Projects
- Predicting student performance from study data
- Image classification with Teachable Machine
- Creating a basic chatbot
6. AI Ethics & Human Impact
- Bias in algorithms
- AI and privacy
- Role of creativity, responsibility, and cross-disciplinary understanding
🔹 Who This Course Is For
- Students in Grades 8–12
- With basic experience in Python and data analytics
- Curious about how AI works and how to build it responsibly
- Interested in careers in technology, design, engineering, economics, or healthcare
🔹 Integrated Pathways
- Academic Enrichment & Future Skills Lab
- Future Readiness & Cross-Disciplinary Application
- University Planning & Application Support – AI-themed portfolio or research project guidance
- Optional connection to AI summer camps, competitions, or high school co-op placements
🔹 Final Outcomes
- Students will build 3–4 small AI/ML projects
- Develop one capstone project (e.g. prediction model, interactive tool, or AI prototype)
- Prepare a presentation or portfolio piece for academic use or competition
- Receive a personalized evaluation and roadmap for deeper study in AI, CS, or data science
