Artificial Intelligence with Python

Module 1: AI Foundations and Problem Solving

• Intelligent agents and environment perception
• Search algorithms (BFS, DFS, A*)
• Heuristic search and adversarial search

Module 2: Knowledge Representation

• Propositional and predicate logic
• Semantic networks and frames
• Rule-based systems and inference engines

Module 3: Python AI Libraries Setup

• NumPy, SciPy, Matplotlib foundations
• Scikit-learn intelligent algorithms
• TensorFlow/PyTorch neural frameworks

Module 4: Machine Learning for AI

• Supervised learning paradigms
• Unsupervised pattern discovery
• Reinforcement learning basics

Module 5: Neural Networks and Deep Learning

• Perceptron to multi-layer perceptrons
• Convolutional networks for vision
• Recurrent networks for sequences

Module 6: Natural Language Processing

• Text processing pipelines
• Part-of-speech tagging, parsing
• Named entity recognition systems

Module 7: Computer Vision Intelligence

• Image processing fundamentals
• Feature extraction techniques
• Object detection and tracking

Module 8: Knowledge-Based Systems

• Expert systems architecture
• Forward/backward chaining inference
• Fuzzy logic and uncertainty handling

Module 9: Planning and Decision Making

• STRIPS planning representation
• Game playing algorithms (Minimax)
• Markov Decision Processes

Module 10: Genetic Algorithms and Optimization

• Evolutionary computation principles
• Selection, crossover, mutation operators
• Multi-objective optimization

Module 11: Reinforcement Learning Agents

• Q-Learning and SARSA algorithms
• Policy gradients and actor-critic
• Deep reinforcement learning

Module 12: AI Ethics and Responsible Development

• Bias detection in intelligent systems
• Transparency and explainability methods
• Safety considerations in deployment

Module 13: Intelligent Web Applications

• AI-powered Flask/FastAPI services
• Chatbot development frameworks
• Recommendation system deployment

Module 14: Advanced AI Architectures

• Transformer models introduction
• Generative adversarial networks
• Transfer learning strategies

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