Module 1: Introduction to Artificial Intelligence

• Basics of Artificial Intelligence (AI)
• History and applications of AI
• Types of AI
• AI in daily life
• Future of AI

Module 2: Computer & Programming Fundamentals

• Basics of computers
• Introduction to programming
• Algorithms and flowcharts
• Problem-solving techniques
• Introduction to Python programming

Module 3: Python for AI

• Variables and data types
• Operators and expressions
• Conditional statements
• Loops
• Functions
• Lists, tuples, and dictionaries
• File handling

Module 4: Data Handling & Visualization

• Introduction to data science
• Data collection methods
• Data cleaning and preprocessing
• Working with datasets
• Charts and graphs
• Data visualization basics

Module 5: Machine Learning Fundamentals

• Introduction to Machine Learning
• Types of Machine Learning:
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Machine learning workflow
• Training and testing data

Module 6: Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Tree
• K-Nearest Neighbor (KNN)
• Naive Bayes
• Clustering basics

Module 7: AI Tools & Applications

• AI chatbots
• Image generation tools
• Voice recognition systems
• AI content creation tools
• AI productivity applications
• Automation tools

Module 8: Introduction to Deep Learning

• Neural Networks basics
• Artificial neurons
• Deep learning concepts
• Applications of deep learning

Module 9: Internet & Cloud Applications

• Cloud computing basics
• Online AI platforms
• Google AI tools
• AI applications on the internet

Module 10: Ethics & Cyber Security in AI

• Responsible AI usage
• AI ethics
• Privacy and security
• Cyber threats and protection

Module 11: Practical Projects

• AI mini projects
• Machine learning model practice
• Data analysis project
• AI presentation work

Module 12: Viva & Certification

• Practical assessment
• Project presentation
• Viva examination