Useful Links
1. Introduction to Chatbots
2. Foundational Technologies
3. Core Components of Conversational AI
4. Chatbot Design Principles
5. Development Methodologies
6. Building NLU Systems
7. Dialogue Management Systems
8. Large Language Models in Chatbots
9. Development Tools and Platforms
10. Integration and Deployment
11. Testing and Quality Assurance
12. Monitoring and Analytics
13. Maintenance and Continuous Improvement
14. Advanced Topics
15. Ethics and Responsible AI
  1. Computer Science
  2. Software Engineering

Chatbot Development

1. Introduction to Chatbots
2. Foundational Technologies
3. Core Components of Conversational AI
4. Chatbot Design Principles
5. Development Methodologies
6. Building NLU Systems
7. Dialogue Management Systems
8. Large Language Models in Chatbots
9. Development Tools and Platforms
10. Integration and Deployment
11. Testing and Quality Assurance
12. Monitoring and Analytics
13. Maintenance and Continuous Improvement
14. Advanced Topics
15. Ethics and Responsible AI
  1. Development Methodologies
    1. Rule-Based Development
      1. Pattern Matching
        1. Regular Expressions
          1. Wildcard Patterns
            1. Template Matching
            2. Decision Trees
              1. Tree Construction
                1. Branching Logic
                  1. Leaf Node Actions
                  2. Finite State Machines
                    1. State Definition
                      1. Transition Rules
                        1. State Management
                      2. Data-Driven Development
                        1. Training Data Collection
                          1. Data Sources
                            1. Annotation Guidelines
                              1. Quality Control
                              2. Model Training
                                1. Feature Engineering
                                  1. Algorithm Selection
                                    1. Hyperparameter Tuning
                                    2. Evaluation and Testing
                                      1. Cross-Validation
                                        1. Performance Metrics
                                          1. Error Analysis
                                        2. Hybrid Approaches
                                          1. Rule-ML Integration
                                            1. Fallback Mechanisms
                                              1. Confidence Thresholding

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                                            6. Building NLU Systems

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