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Course Outline
Introduction to Virtual Assistants <\/p>
- Overview of virtual assistant technologies and applications <\/li>
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Introduction to conversational AI and its significance
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ChatGPT Basics <\/p>
- Understanding ChatGPT and its underlying architecture <\/li>
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Exploring different versions and configurations of ChatGPT
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Building Conversational Flows <\/p>
- Designing conversational flows for virtual assistants <\/li>
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Implementing user input handling and system responses
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Natural Language Understanding (NLU) with ChatGPT <\/p>
- Leveraging NLU techniques to extract intent and entities <\/li>
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Training ChatGPT for intent recognition and entity extraction
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Personalization and User Context <\/p>
- Enhancing virtual assistants with personalized responses <\/li>
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Managing user context and maintaining conversational context
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Fine-tuning ChatGPT Models <\/p>
- Techniques for fine-tuning ChatGPT models for virtual assistant tasks <\/li>
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Leveraging domain-specific data for better performance
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Best Practices and Optimization <\/p>
- Optimizing virtual assistants for efficiency and scalability <\/li>
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Handling common challenges and mitigating biases in virtual assistants
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Ethics and Responsible AI <\/p>
- Ensuring ethical and responsible use of ChatGPT in virtual assistants <\/li>
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Addressing privacy concerns and data protection in virtual assistant applications
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Real-world Use Cases <\/p>
- Examining successful virtual assistant implementations <\/li>
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Case studies and examples from various industries
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Summary and Next Steps <\/p>
- Recap of key concepts and takeaways from the course <\/li>
- Resources and recommendations for further learning and exploration <\/li> <\/ul>
Requirements
- Fundamental computer literacy <\/li>
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Familiarity with virtual assistant concepts and technologies
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Target Audience <\/p>
- Virtual assistant developers <\/li>
- Chatbot developers <\/li>
- AI enthusiasts <\/li> <\/ul>
14 Hours
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
