1. Introduction to Artificial Intelligence

• Definition and History of AI
• Types of AI: Narrow AI, General AI, and Superintelligence
• AI vs Machine Learning vs Deep Learning
• Applications of AI in various industries (healthcare, finance, robotics, etc.)

2. Mathematics for AI

• Linear Algebra (vectors, matrices, eigenvalues)
• Probability and Statistics (Bayes’ Theorem, probability distributions)
• Calculus (derivatives, integrals)
• Basic concepts in graph theory

3. Programming Foundations for AI

• Introduction to Python for AI
o Variables, Loops, and Functions
o Libraries for AI: NumPy, Pandas, Matplotlib
• Jupyter Notebooks for coding and experimentation

4. Machine Learning Fundamentals

• Supervised Learning
o Regression (Linear, Logistic)
o Classification (K-Nearest Neighbors, Decision Trees, SVM)
• Unsupervised Learning
o Clustering (K-Means, Hierarchical Clustering)
o Dimensionality Reduction (PCA)
• Reinforcement Learning Basics

5. Neural Networks and Deep Learning Basics

• Introduction to Neural Networks
• Activation Functions (Sigmoid, ReLU, etc.)
• Feedforward and Backpropagation
• Introduction to TensorFlow or PyTorch

6. Natural Language Processing (NLP)

• Basics of NLP and Text Processing
• Tokenization, Stemming, and Lemmatization
• Sentiment Analysis
• Introduction to Transformers and BERT

7. AI Algorithms and Techniques

• Search Algorithms: A*, BFS, DFS
• Optimization Algorithms: Genetic Algorithms, Gradient Descent
• Game Playing (Minimax, Alpha-Beta Pruning)

8. AI Ethics and Bias

• Ethical Considerations in AI
• Fairness and Transparency in AI Models
• Bias in AI: How to mitigate bias in datasets and algorithms
• AI's Social and Economic Impact

9. AI Tools and Frameworks

• TensorFlow and Keras for building AI models
• Scikit-Learn for traditional machine learning
• OpenCV for Computer Vision basics
• Hugging Face for NLP models

10. Project Work

• Hands-on project ideas for beginners:
o Image classification using a basic Convolutional Neural Network (CNN)
o Building a Chatbot with NLP techniques
o Creating a recommendation system (content-based or collaborative filtering)

11. Capstone Project

• Applying all learned concepts to solve a real-world AI problem, such as:
o Predicting house prices using supervised learning
o Sentiment analysis on social media data
o Implementing a simple game AI