·        What is AI?

·        History and evolution of AI

·        Real-world applications of AI

·        AI vs Machine Learning vs Deep Learning

·        Ethics and risks in AI

·        Types of AI: Narrow, General, Super AI

·        Understanding intelligent agents

·        Basics of search algorithms (BFS, DFS)

·        Heuristics and problem-solving

·        Importance of data in AI

·        Types of data: structured vs unstructured

·        Data collection and cleaning

·        Feature selection and extraction

·        Introduction to data visualization tools

·        Supervised vs Unsupervised Learning

·        Regression and classification basics

·        Common algorithms: Linear Regression, KNN, Decision Trees

·        Evaluation metrics: accuracy, precision, recall, F1-score

·        What is NLP and why it matters in AI

·        Text preprocessing techniques

·        Tokenization, stemming, lemmatization

·        Intro to sentiment analysis and chatbots

·        AI in healthcare, finance, education, and security

·        Recommendation systems

·        Image and speech recognition

·        Autonomous systems (e.g., self-driving cars)

·        Mini Project: Basic AI Chatbot

·        Use Case: Predicting Customer Churn

·        Case Study: AI in Smart Assistants (Alexa, Siri)

·        Course review and Q&A

·        Final quiz (MCQ-based)

·        Evaluation of project submissions

·        Certification ceremony and next steps