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Duration 21 hours
Course Outline
Foundations of AI Assistants
- A broad look at conversational AI and the role of virtual assistants
- Current trends shaping AI-driven human-computer interaction
- Exploring diverse use cases across various sectors
Designing Conversational AI & UI/UX
- Core tenets of human-centered AI design
- Constructing chatbot logic and mapping user interactions
- Creating AI assistant mockups with tools like Figma or comparable platforms
NLP and Contextual Intelligence
- Diving into NLP architectures, including transformers, embeddings, and intent detection
- Techniques for entity extraction and maintaining conversation context
- Managing multi-turn dialogues to ensure deep contextual comprehension
Development Frameworks for AI Assistants
- Selecting the optimal framework: Dialogflow, Rasa, or the OpenAI API
- Building AI-driven dialogue sequences
- Incorporating speech-to-text and text-to-speech features
API Connectivity and Integrations
- Linking AI assistants to external APIs and data repositories
- Synchronizing with messaging channels such as Slack and WhatsApp
- Adopting security best practices for AI-driven solutions
Deployment Strategies and Ongoing Maintenance
- Hosting AI assistants on cloud infrastructure
- Tracking and enhancing performance through analytical tools
- Implementing continuous model updates and fine-tuning protocols
Real-World Project Execution
- Assembling a working AI assistant prototype
- Rigorous testing, debugging, and optimization for end-users
- Final rollout and strategies for future enhancements
Requirements
- A fundamental grasp of artificial intelligence and machine learning principles
- Working knowledge of at least one programming language, such as Python or JavaScript
- A solid understanding of UI/UX design fundamentals (specifically for designers)
Target Participants
- Professionals in AI development
- UI/UX design specialists
- Individuals with a strong interest in conversational AI
