## Module 1: Introduction to AI
Start by explaining what Artificial Intelligence is and where it is used.
Topics:
* What AI is
* Real-world AI applications
* Difference between AI, Machine Learning, and Data Science
Main tool introduced:
* Python
Goal: Students understand the **purpose of AI**.
## Module 2: Python Programming for AI
Students learn programming fundamentals.
Topics:
* Variables
* Loops
* Functions
* Lists and dictionaries
Small exercise:
Create a program that calculates the **average score of students**.
Goal: Students become comfortable writing Python code
## Module 3: Data Analysis
Teach students how to work with data.
Tools used:
* Pandas
* NumPy
Topics:
* Loading datasets
* Cleaning data
* Filtering and analyzing data
Mini project:
Analyze **student exam scores dataset**.
## Module 4: Data Visualization
Students learn how to visualize patterns in data.
Tool:
* Matplotlib
Topics:
* Line charts
* Bar charts
* Scatter plots
Mini project:
Show the relationship between **study hours and exam scores**.
## Module 5: Machine Learning
Students start building prediction models.
Tool:
* Scikit-learn
Topics:
* Regression
* Classification
* Model training
* Model evaluation
Project:
Predict **student performance using machine learning**.
## Module 6: Deep Learning
Introduce advanced AI systems.
Tool:
* TensorFlow
Topics:
* Neural networks
* AI model training
* Predictions
Mini exercise:
Create a **basic neural network model**.
## Module 7: AI Projects
Students build real-world applications.
Projects:
1. Student performance prediction
2. Bank fraud detection system
3. Simple chatbot for answering questions
Students apply everything they learned.
## Final Module: Deployment
Students learn how to share their AI projects online.
Tool:
* Flask
Goal:
Create a **simple AI web application**.
✅ **Result of this course**
After completing the course, students will be able to:
* Analyze data
* Build machine learning models
* Create AI applications
* Work on real Project
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