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Course Outline
Introduction to Conversational AI
- The history and evolution of voice assistants.
- Key components: Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Dialogue Management, and Text-to-Speech (TTS).
- Overview of major platforms: Alexa, Google Assistant, and Rasa.
Designing Voice Interfaces
- Principles of conversational user experience (UX).
- Intent modeling and entity extraction techniques.
- Voice design tools and flowcharting methods.
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 session states.
Building Voice Assistants with Rasa
- Rasa architecture: NLU, Core, and Actions.
- Configuring training data and domain files.
- Implementing custom actions, forms, and contextual dialogues.
Integrating Voice Assistants
- Utilizing APIs and webhook backend services.
- Connecting to CRMs, databases, and external applications.
- Implementing voice assistants in web apps, IoT devices, and mobile platforms.
Testing, Deployment, and Optimization
- Using simulators and test cases for voice interactions.
- Monitoring usage metrics and debugging conversations.
- Deploying to Google Assistant, Alexa devices, or private platforms.
Security, Compliance, and Scalability
- User authentication and authorization protocols for assistants.
- Data privacy considerations, GDPR compliance, and audit trails.
- Version control and CI/CD pipelines for voice applications.
Summary and Next Steps
Requirements
- A solid understanding of RESTful APIs and JSON.
- Experience with at least one programming language (e.g., Python or JavaScript).
- Familiarity with core concepts of natural language processing.
Target Audience
- Software developers.
- UX designers focusing on voice-based interfaces.
- Conversational AI teams responsible for developing virtual assistants.
21 Hours
