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Course Outline
Introduction
- Conversational AI systems overview
- Evolution and components of modern conversational systems
Designing Advanced Conversational Flows
- Creating dynamic, context-aware dialogues
- Handling complex user intents and entities
- Building and testing adaptive conversation scenarios
Advanced NLP Techniques
- Pre-training and fine-tuning large language models
- Implementing named entity recognition (NER) and sentiment analysis
Multilingual and Cross-Language Processing
- Strategies for supporting multiple languages in one 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
- Using databases and cloud services for data storage and retrieval
Security and Compliance Considerations
- Ensuring data privacy, encryption, and secure user interactions
- Developing API connections and implementing data security protocols
Designing User-Centric Interfaces
- Enhancing user experience with voice and visual interactions
Adaptive Learning for Conversational AI
- Implementing user feedback loops and learning mechanisms to improve interactions
- Building adaptive learning features and evaluating their performance
Managing Conversational AI Projects
- Agile project management techniques specific to AI projects
- Defining KPIs and success metrics for conversational projects
Testing and Optimization Strategies
- Continuous testing frameworks for conversational AI
- Monitoring, analytics, and refining models post-deployment
- Conducting performance tests and optimization routines
Summary and Next Steps
Requirements
- Fundamental understanding of conversational AI and NLP models
- Experience with programming languages such as Python
- Basic knowledge of API integration and cloud services
Audience
- AI project managers
- Conversational AI developers
- Senior software engineers
35 Hours
Testimonials (1)
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