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Course Outline

Introduction to Conversational AI

  • The history and evolution of voice assistants
  • Core components: ASR, NLU, Dialogue Management, TTS
  • Overview of leading platforms: Alexa, Google Assistant, Rasa

Designing Voice Interfaces

  • Principles of conversational UX
  • Intent modeling and entity extraction
  • Voice design tools and flowcharting techniques

Developing with Dialogflow and Alexa

  • Dialogflow agents, intents, and webhook fulfillment
  • Alexa Skills: intents, slots, voice models, and endpoint integration
  • Managing multi-turn conversations and sessions

Building Voice Assistants with Rasa

  • Rasa architecture: NLU, Core, and Actions
  • Configuration of training data and domains
  • Implementing custom actions, forms, and contextual dialogues

Integrating Voice Assistants

  • Webhook back-end services and APIs
  • Linking to CRMs, databases, and external applications
  • Utilizing voice assistants in web apps, IoT, and mobile environments

Testing, Deployment, and Optimization

  • Simulators and test cases for voice interactions
  • Monitoring usage patterns and debugging conversations
  • Deployment to Google Assistant, Alexa devices, or private platforms

Security, Compliance, and Scalability

  • User authentication and authorization for assistants
  • Data privacy, GDPR, and audit trails
  • Version control and CI/CD pipelines for voice applications

Summary and Next Steps

Requirements

  • Knowledge of RESTful APIs and JSON
  • Experience with at least one programming language (such as Python or JavaScript)
  • Understanding of natural language processing concepts

Target Audience

  • Software developers
  • UX designers focusing on voice-based interfaces
  • Conversational AI teams developing virtual assistants
 21 Hours

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