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