Defining AI: Its history, evolution, and core concepts.

Human Intelligence vs. Artificial Intelligence: Key differences.

AI Applications across sectors: Healthcare, Business, Education, and Industry.

Narrow AI vs. General AI.

Machine Learning (ML) & Deep Learning.

Natural Language Processing (NLP).

Computer Vision technology.

Core concepts of Machine Learning.

Types of Learning: Supervised, Unsupervised, and Reinforcement Learning.

How to train models on data.

Practical Workshop: Applied examples using free tools like Google Teachable Machine.

Understanding why data is the “fuel” for AI engines.

Data collection, cleaning, and analysis techniques.

Principles of statistical data analysis.

Ethics: Data privacy and ethical usage in AI.

Smart Assistants: (ChatGPT, Siri, Alexa).

Image and video analysis technologies.

Recommendation Engines: (YouTube, Netflix, Amazon algorithms).

Robotics and Autonomous Driving systems.

Automation and improving operational efficiency.

AI-driven decision support systems.

Applications in Marketing, Human Resources (HR), and Services.