Are you building with Claude but unsure if you’re doing it right?
Whether you’re experimenting with prompt engineering or deploying Claude in a production-grade environment, knowing how to wield its power properly can save you weeks of guesswork—and thousands in engineering hours.
Anthropic just made that a whole lot easier.
They’ve released six free, no-fluff courses built by Claude’s own engineers, packed with real-world examples, tested code, and advanced implementation strategies. From API fundamentals to next-gen Model Context Protocol (MCP) development, these resources are designed to help developers, AI engineers, and product teams ship faster and smarter with Claude.
Here’s what each course offers:
Anthropic’s 6 Free Hands-On Courses: A Complete Guide
Anthropic has released six comprehensive courses designed to help developers master Claude AI integration across different platforms and use cases. These courses range from foundational API usage to advanced agent architectures, providing practical skills for building production-ready AI applications.
1. Claude with the Anthropic API
Learn to work directly with Claude’s API—mastering prompt construction, model parameters, and output formatting for a range of real-world tasks.
- Best for: Software engineers building AI-powered applications from scratch
- Duration: 87 lessons across 7 sections
- Prerequisites: Python programming, JSON handling, Anthropic API key
This foundational course covers everything you need to integrate Claude directly through Anthropic’s API. You’ll start with basic authentication and API requests, then progress through conversation management, structured output generation, and advanced features like streaming responses.
The course emphasizes practical implementation patterns for real-world applications. You’ll learn to build and evaluate prompts systematically using automated testing pipelines, create custom tools for external service integration, and implement Retrieval Augmented Generation (RAG) systems with hybrid search capabilities.
Advanced topics include the Model Context Protocol (MCP) for connecting Claude to various data sources, and comprehensive coverage of agent architectures for autonomous AI systems. The course also covers Claude Code and Computer Use features, showing how to automate development workflows and UI interactions.
Key learning outcomes:
- Master API authentication and multi-turn conversations
- Build systematic prompt evaluation frameworks
- Implement custom tools and function calling
- Design production-grade RAG systems with reranking
- Create flexible agent workflows with parallel execution
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2. Claude with Amazon Bedrock
If you’re deploying Claude in an enterprise AWS environment, this course walks you through Bedrock-specific workflows and implementation patterns that ensure scalability, performance, and compliance.
- Best for: Enterprise developers deploying Claude in AWS environments
- Duration: 81 lessons across 6 sections
- Prerequisites: Python programming, JSON handling, AWS account with Bedrock access
This course focuses specifically on using Claude through AWS Bedrock, making it ideal for organizations already invested in the AWS ecosystem. You’ll learn to make API calls using boto3, implement enterprise-grade features, and leverage AWS-specific optimizations for scalability and compliance.
The curriculum covers the same core concepts as the API course but with AWS-specific implementations. You’ll work with streaming responses, structured data extraction, and multi-step tool execution workflows within the Bedrock environment.
Enterprise features receive special attention, including prompt caching for cost optimization, extended thinking capabilities for complex reasoning tasks, and image processing workflows. The course also covers automated testing strategies and UI interaction patterns specifically designed for AWS deployments.
Key learning outcomes:
- Configure Claude models via AWS Bedrock using boto3
- Implement enterprise-grade conversation management
- Build cost-optimized RAG systems with AWS services
- Master prompt caching and extended thinking features
- Deploy automated testing and computer use workflows
3. Claude with Google Vertex AI
Integrate Claude within GCP-native workflows, automate LLM pipelines, and learn how to scale across multiple services using Vertex AI. Ideal for developers building AI-enabled apps on Google Cloud.
- Best for: Developers building AI applications on Google Cloud Platform
- Duration: 75 lessons across 7 sections
- Prerequisites: Python programming, JSON handling, Google Cloud account with Vertex AI access
This course adapts Claude integration for Google Cloud’s Vertex AI platform, focusing on GCP-native workflows and multi-service integration. You’ll learn to authenticate and configure Claude through Vertex AI using the Anthropic SDK, with emphasis on selecting appropriate models based on intelligence, speed, and cost considerations.
The course covers systematic prompt evaluation using objective scoring metrics, essential for production deployments. You’ll implement tool calling for web search, file operations, and custom functionality, all optimized for the GCP environment.
Advanced features include extended thinking capabilities, citation systems, and prompt caching strategies. The course also covers MCP integration for connecting Claude to external services and designing both deterministic workflows and flexible agent systems.
Key learning outcomes:
- Set up Claude authentication through Vertex AI
- Implement objective prompt evaluation metrics
- Build GCP-optimized RAG pipelines with hybrid search
- Use advanced features like extended thinking and citations
- Design scalable workflows and agent architectures
4. Introduction to Model Context Protocol (MCP)
Get hands-on with the new Model Context Protocol, a framework for building structured, tool-using agents using Claude. You’ll build your first MCP server using Python and Claude tools.
Best for: Engineers wanting to integrate Claude with external tools and services- Duration: 16 lessons across 2 sections
- Prerequisites: Basic Python programming, understanding of async/await patterns, API concepts
MCP represents a breakthrough in AI integration, allowing developers to connect Claude to external services without writing extensive boilerplate code. This course introduces the protocol’s architecture and client-server communication model, showing how to build both MCP servers that expose tools and MCP clients that consume them.
You’ll start by understanding MCP’s architecture and building your first MCP server using the Python SDK. The course includes hands-on projects, including implementing a complete document management system using MCP principles.
The curriculum covers creating resources for direct data access and prompts for pre-defined workflows. You’ll learn to test and debug MCP servers using the built-in MCP Inspector, and understand when to choose between tools, resources, and prompts based on different control patterns.
Key learning outcomes:
- Understand MCP architecture and communication protocols
- Build MCP servers that expose tools using Python SDK
- Implement MCP clients for application integration
- Create resources and prompts for workflow optimization
- Master testing and debugging with MCP Inspector
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5. MCP Advanced Topics
Go beyond the basics: this course dives into advanced agent workflows including sampling strategies, notifications, local file systems, and production-grade transport mechanisms.
- Best for: Engineers building production MCP servers requiring advanced capabilities
- Duration: 15 lessons across 2 sections
- Prerequisites: Basic understanding of MCP servers and clients, async programming familiarity
This advanced course dives deep into production-grade MCP implementations, covering technical aspects from message passing to deployment strategies. You’ll learn how MCP enables language models to interact with external tools through standardized protocols, transports, and message formats.
The course emphasizes advanced server features including tool functions, logging systems, and progress notifications for better user experience. You’ll handle bidirectional communication between MCP clients and servers, essential for complex production workflows.
File system access receives detailed coverage through the roots permission model, ensuring secure and controlled access to local resources. The course covers both stdio and HTTP transports, helping you choose the right approach for local development versus remote deployments.
Key learning outcomes:
- Implement advanced MCP servers with logging and notifications
- Handle bidirectional client-server communication
- Configure secure file system access using roots permissions
- Work with stdio and HTTP transports for different deployment scenarios
- Implement sampling callbacks for server-initiated LLM requests
6. Claude Code in Action
See Claude in motion with real terminal-based workflows. Learn how to orchestrate multi-step agentic tasks, integrate with developer tools, and optimize for rapid iteration.
- Best for: Engineers wanting to accelerate development workflows with AI assistance
- Duration: 10 lessons in 1 section
- Prerequisites: Basic command line interface familiarity, Claude Code access, API key
Claude Code transforms development workflows by providing a command-line AI assistant that can read files, execute commands, and modify code through its integrated tool system. This course shows Claude in action with real terminal-based workflows and multi-step agentic tasks.
You’ll learn to use Claude Code’s core tools for file manipulation, command execution, and code analysis. Context management receives special attention, covering techniques like /init commands, Claude.md files, and @ mentions for maintaining conversation context across complex tasks.
The course covers advanced features including Plan Mode and Thinking Mode for complex tasks requiring deeper analysis. You’ll create custom commands for automating repetitive development workflows and extend Claude Code with MCP servers for browser automation and other capabilities.
Key learning outcomes:
- Master Claude Code’s core tools for development tasks
- Implement effective context management strategies
- Use Plan Mode and Thinking Mode for complex analysis
- Create custom commands for workflow automation
- Integrate MCP servers for extended capabilities
- Set up GitHub integration for automated PR reviews
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Why These Courses Matter
These courses address the growing need for practical AI integration skills in software development. Rather than theoretical overviews, they provide hands-on experience with real-world implementation patterns. The progressive structure allows developers to start with basic API usage and advance to sophisticated agent architectures.
The emphasis on different platforms (direct API, AWS Bedrock, Google Vertex AI) ensures developers can choose the approach that best fits their existing infrastructure. The dedicated MCP courses reflect the protocol’s importance in building modular, maintainable AI applications.
Each course includes practical projects, code examples, and testing strategies essential for production deployments. The focus on evaluation frameworks and systematic prompt engineering helps developers build reliable, scalable AI applications rather than fragile prototypes.
✅ Built by Claude’s engineering team
✅ Includes runnable code + hands-on exercises
✅ Features real enterprise use cases
✅ Provides certificates upon completion
✅ 100% free and self-paced
If you’re serious about shipping with Claude—whether you’re an engineer, product manager, or founder—these courses are your fast track to mastery.
👉 Pick a course, start building smarter, and stay ahead of the curve.
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