fix all image links to use dataset repo

Author: burtenshaw

units/en/unit0/introduction.mdx

@@ -1,6 +1,6 @@
 # Welcome to the 🤗 Model Context Protocol (MCP) Course
 
-![MCP Course thumbnail](https://huggingface.co/mcp-course/images/resolve/main/unit0/1.png)
+![MCP Course thumbnail](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/1.png)
 
 Welcome to the most exciting topic in AI today: **Model Context Protocol (MCP)**!
 
@@ -98,7 +98,7 @@ Each chapter in this course is designed **to be completed in 1 week, with approx
 
 Since there's a deadline, we provide you a recommended pace:
 
-![Recommended Pace](https://huggingface.co/mcp-course/images/resolve/main/unit0/2.png)
+![Recommended Pace](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/2.png)
 
 ## How to get the most out of the course?
 
@@ -108,7 +108,7 @@ To get the most out of the course, we have some advice:
 2. **Do the quizzes and assignments**: the best way to learn is through hands-on practice and self-assessment.
 3. **Define a schedule to stay in sync**: you can use our recommended pace schedule below or create yours.
 
-![Course advice](https://huggingface.co/mcp-course/images/resolve/main/unit0/3.png)
+![Course advice](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/3.png)
 
 ## Who are we
 

units/en/unit1/architectural-components.mdx

@@ -6,7 +6,7 @@ In the previous section, we discussed the key concepts and terminology of MCP. N
 
 The Model Context Protocol (MCP) is built on a client-server architecture that enables structured communication between AI models and external systems. 
 
-![MCP Architecture](https://huggingface.co/mcp-course/images/resolve/main/unit1/4.png)
+![MCP Architecture](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/4.png)
 
 The MCP architecture consists of three primary components, each with well-defined roles and responsibilities: Host, Client, and Server. We touched on these in the previous section, but let's dive deeper into each component and their responsibilities.
 

units/en/unit1/communication-protocol.mdx

@@ -16,7 +16,7 @@ At its core, MCP uses **JSON-RPC 2.0** as the message format for all communicati
 - Language-agnostic, supporting implementation in any programming environment
 - Well-established, with clear specifications and widespread adoption
 
-![message types](https://huggingface.co/mcp-course/images/resolve/main/unit1/5.png)
+![message types](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/5.png)
 
 The protocol defines three types of messages:
 

units/en/unit1/gradio-mcp.mdx

@@ -79,7 +79,7 @@ http://your-server:port/gradio_api/mcp/sse
 
 The application itself will still be accessible and it looks like this:
 
-![Gradio MCP Server](https://huggingface.co/mcp-course/images/resolve/main/unit1/7.png)
+![Gradio MCP Server](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/7.png)
 
 ## How It Works Behind the Scenes
 

units/en/unit1/key-concepts.mdx

@@ -17,15 +17,15 @@ The **M×N Integration Problem** refers to the challenge of connecting M differe
 
 Without a protocol like MCP, developers would need to create M×N custom integrations—one for each possible pairing of an AI application with an external capability. 
 
-![Without MCP](https://huggingface.co/mcp-course/images/resolve/main/unit1/1.png)
+![Without MCP](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/1.png)
 
 Each AI application would need to integrate with each tool/data source individually. This is a very complex and expensive process which introduces a lot of friction for developers, and high maintenance costs.
 
 ### With MCP (M+N Solution)
 
 MCP transforms this into an M+N problem by providing a standard interface: each AI application implements the client side of MCP once, and each tool/data source implements the server side once. This dramatically reduces integration complexity and maintenance burden.
 
-![With MCP](https://huggingface.co/mcp-course/images/resolve/main/unit1/2.png)
+![With MCP](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/2.png)
 
 Each AI application implements the client side of MCP once, and each tool/data source implements the server side once.
 
@@ -45,7 +45,7 @@ When documenting our applications and communincating with the community, we shou
 
 Just like client server relationships in HTTP, MCP has a client and a server.
 
-![MCP Components](https://huggingface.co/mcp-course/images/resolve/main/unit1/3.png)
+![MCP Components](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/3.png)
 
 - **Host**: The user-facing AI application that end-users interact with directly. Examples include Anthropic's Claude Desktop, AI-enhanced IDEs like Cursor, inference libraries like Hugging Face Python SDK, or custom applications built in libraries like LangChain or smolagents. Hosts initiate connections to MCP Servers and orchestrate the overall flow between user requests, LLM processing, and external tools.
 

units/en/unit1/sdk.mdx

@@ -141,7 +141,7 @@ You can then open the MCP Inspector at [http://127.0.0.1:6274](http://127.0.0.1:
 
 You'll see the server's capabilities and the ability to call them via the UI.
 
-![MCP Inspector]([./images/mcp-inspector.png](https://huggingface.co/mcp-course/images/resolve/main/unit1/6.png)
+![MCP Inspector]([./images/mcp-inspector.png](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/6.png)
 
 ## MCP SDKs
 

units/en/unit2/introduction.mdx

@@ -40,7 +40,7 @@ Before proceeding with this unit, make sure you:
 
 We'll build a sentiment analysis application that consists of three main parts: the server, the client, and the deployment.
 
-![sentiment analysis application](https://huggingface.co/mcp-course/images/resolve/main/unit2/1.png)
+![sentiment analysis application](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit2/1.png)
 
 ### Server Side