Curriculum:
1. Introduction to Articial Intelligence
Definition and historical context
Types of AI: Narrow vs. General
Real-world examples across industries
Components of a computer & basics of OS
2. Problem Solving
Algorithmic thinking
Introduction to owcharts and pseudocode
3. Mathematics for AI
Basic algebra
Probability and statistics
Vectors, matrices, and operations
Applications in AI
4. Introduction to Machine Learning
Definitions and key concepts
Supervised vs. Unsupervised learning
Overview of regression, classication, and clustering
Decision trees and random forests
5. Practical Application – No-code AI Tools
Introduction to No-code/Low-code Platforms
Explore tools like Google Teachable
Machine, IBM Watson Studio
Hands-on projects with no coding
6. Introduction to Coding for AI
Introduction to a Python
Variables, loops, and conditionals
7. Hands-on AI Coding Projects
Implementing simple machine learning algorithms
Solving basic problems using code
8. Final Project
Apply theoretical knowledge to a practical project
Present ndings and demonstrate understanding
9. Assessment and Wrap-up