1. Introduction to AI
Definition and history of AI
AI vs. Machine Learning vs. Deep Learning
AI applications in different industries
2. Foundations of AI
Problem-solving and search algorithms
Heuristics and optimization
Knowledge representation and reasoning
3. Machine Learning Basics
Supervised vs. Unsupervised Learning
Regression and Classification models
Evaluation metrics (accuracy, precision, recall, F1-score)
4. Deep Learning & Neural Networks
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
5. Natural Language Processing (NLP)
Text processing and tokenization
Sentiment analysis and chatbots
Transformer models (e.g., GPT, BERT)
6. AI Ethics & Bias
Fairness and bias in AI
Explainability and interpretability
AI governance and regulations
7. AI in the Real World
AI in healthcare, finance, and gaming
AI for automation and robotics
Future trends in AI
8. Hands-on AI Projects
Building a chatbot
Image classification with deep learning
Predictive modeling with machine learning