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Course Outline
Introduction
- Overview of conversational AI systems.
- The 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 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.
- 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
- A fundamental understanding of conversational AI and NLP models.
- Experience with programming languages such as Python.
- Basic knowledge of API integration and cloud services.
Target Audience
- AI project managers.
- Conversational AI developers.
- Senior software engineers.
35 Hours
