• What is AI?
• History and Evolution of AI
• Types of AI (Narrow AI, General AI, Superintelligent AI)
• Applications of AI
• Key Concepts and Terminology

• Python Basics for AI
• Libraries for AI: NumPy, Pandas, Matplotlib
• Data Handling and Visualization
• Introduction to Scikit-Learn

• Supervised Learning vs. Unsupervised Learning
• Key Algorithms: Linear Regression, Logistic Regression, Decision Trees, K-Means Clustering
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
• Cross-Validation and Hyperparameter Tuning

• Ensemble Learning: Random Forests, Gradient Boosting
• Support Vector Machines (SVM)
• Principal Component Analysis (PCA)
• Model Deployment and Monitoring

• Introduction to Neural Networks
• Architecture of Neural Networks
• Activation Functions
• Training Neural Networks: Backpropagation, Gradient Descent
• Introduction to TensorFlow and Keras
• Building and Training Deep Learning Models

• Text Processing Techniques
• Word Embeddings: Word2Vec, GloVe
• NLP Tasks: Text Classification, Named Entity Recognition
• Sentiment Analysis
• Sequence-to-Sequence Models and Transformers

• Basics of Computer Vision
• Image Processing Techniques
• Convolutional Neural Networks (CNNs)
• Object Detection and Image Classification
• Transfer Learning for Computer Vision

• Introduction to Reinforcement Learning
• Key Concepts: Agents, Environments, Rewards
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradients and Actor-Critic Methods

• Ethical Considerations in AI
• Bias and Fairness in AI Models
• Privacy and Security Concerns

Responsible AI Practices

• Defining an AI Problem
• Data Collection and Preparation
• Model Selection and Training
• Evaluation and Optimization
• Project Presentation and Deployment