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Course Outline
Introduction to DeepSeek APIs
- Overview of DeepSeek APIs and their capabilities
- Understanding API endpoints and authentication
- Comparing DeepSeek APIs with other AI model APIs
Setting Up the Development Environment
- Installing required tools and libraries
- Setting up API keys and authentication methods
- Making test API calls using Postman or cURL
Integrating DeepSeek APIs into Applications
- Calling DeepSeek APIs from Python and JavaScript
- Handling API responses and error management
- Implementing AI-powered features in web and mobile apps
Building AI Chatbots with DeepSeek APIs
- Designing chatbot workflows and user interactions
- Implementing real-time conversational AI
- Enhancing chatbot responses with context awareness
AI-Powered Automation and Workflows
- Automating repetitive tasks using DeepSeek APIs
- Integrating AI with existing enterprise workflows
- Case studies of AI-powered automation
Optimizing API Performance
- Managing API rate limits and optimizing API calls
- Implementing caching and response handling strategies
- Ensuring security and compliance in API usage
Deploying and Scaling AI Applications
- Deploying AI applications in cloud environments
- Scaling AI applications for high traffic
- Monitoring and maintaining AI-driven applications
Summary and Next Steps
Requirements
- Experience with API integration
- Proficiency in a programming language (eg, Python, JavaScript)
- Basic understanding of machine learning concepts
Audience
- Developers building AI-powered applications
- Software engineers integrating AI models into workflows
- Data scientists leveraging DeepSeek APIs for automation
14 Hours