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Duration 35 hours
Course Outline
Introduction
- Overview of Conversational AI systems
- The evolution and core components of modern conversational systems
Designing Advanced Conversational Flows
- Creating dynamic, context-aware dialogues
- Managing complex user intents and entities
- Constructing and testing adaptive conversation scenarios
Advanced NLP Techniques
- Pre-training and fine-tuning large language models
- Applying named entity recognition (NER) and sentiment analysis
Multilingual and Cross-Language Processing
- Strategies for supporting multiple languages within a single project
- Integrating and testing NER and sentiment analysis in a conversational bot
Backend Integration and Data Handling
- Connecting bots to enterprise-level data sources and APIs
- Utilizing databases and cloud services for data storage and retrieval
Security and Compliance Considerations
- Guaranteeing data privacy, encryption, and secure user interactions
- Establishing API connections and enforcing data security protocols
Designing User-Centric Interfaces
- Enhancing user experience through voice and visual interactions
Adaptive Learning for Conversational AI
- Implementing user feedback loops and learning mechanisms to refine interactions
- Developing adaptive learning features and assessing their performance
Managing Conversational AI Projects
- Agile project management techniques tailored to AI projects
- Defining KPIs and success metrics for conversational initiatives
Testing and Optimization Strategies
- Continuous testing frameworks for conversational AI
- Monitoring, analytics, and post-deployment model refinement
- Conducting performance tests and optimization routines
Summary and Next Steps
Requirements
- A solid grasp of conversational AI and NLP models
- Proficiency in programming languages such as Python
- Foundational knowledge of API integration and cloud services
Audience
- AI project managers
- Conversational AI developers
- Senior software engineers
