Module 1: Introduction to AI
What is Artificial Intelligence; History of AI; AI vs Machine Learning vs Deep Learning; Applications of AI in daily life and business
Module 2: Data and AI Basics
Types of data (structured, unstructured, semi-structured); Data preprocessing; Introduction to datasets for AI; Understanding features and labels
Module 3: Machine Learning Overview
Supervised, unsupervised, and reinforcement learning; Common algorithms (Linear Regression, Decision Trees, K-Means); Training and testing models
Module 4: Deep Learning Basics
Introduction to neural networks; Perceptrons and activation functions; Overview of CNNs, RNNs; AI frameworks (TensorFlow, PyTorch)
Module 5: Natural Language Processing (NLP)
Text data preprocessing; Tokenization, stemming, and lemmatization; Sentiment analysis; Chatbots and language models
Module 6: Computer Vision
Image processing basics; Object detection and recognition; Convolutional Neural Networks (CNN) applications
Module 7: AI Ethics and Governance
Bias in AI; Privacy concerns; Responsible AI principles; AI regulations and guidelines
Module 8: AI in Business and Society
AI for decision-making; Automation of processes; AI in healthcare, finance, marketing, and education; Future trends
Module 9: AI Tools and Platforms
Cloud AI platforms (Azure AI, Google AI, AWS SageMaker); AutoML; AI visualization tools; Deploying AI models
Module 10: AI Project & Capstone
End-to-end AI project workflow; Data collection, preprocessing, model selection, evaluation, deployment