diff --git a/Labs/API_Labs/FastAPI_Labs/README.md b/Labs/API_Labs/FastAPI_Labs/README.md
index 6b75e58d..77d32979 100644
--- a/Labs/API_Labs/FastAPI_Labs/README.md
+++ b/Labs/API_Labs/FastAPI_Labs/README.md
@@ -9,7 +9,7 @@
 
 In this Lab, we will learn how to expose ML models as APIs using [FastAPI](https://fastapi.tiangolo.com/) and [uvicorn](https://www.uvicorn.org/).
 1. **FastAPI**: FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints.
-2. **uvicorn**: Uvicorn is an [ASGI](https://youtu.be/vKjCkeJGbNk) web server implementation for Python. It is often used to serve FastAPI aplications.
+2. **uvicorn**: Uvicorn is an [Asynchronous Server Gateway Interface - ASGI](https://youtu.be/vKjCkeJGbNk) web server implementation for Python. It is often used to serve FastAPI aplications.
 
 The workflow involves the following steps:
 1. Training a Decision Tree Classifier on Iris Dataset.
@@ -147,4 +147,4 @@ FastAPI will catch this exception and return a response with a 404 status code a
     "detail": "Item with ID 1 not found"
 }
 ```
-- For more information on how to handle errors in FASTAPI refer to this [documentation](https://fastapi.tiangolo.com/tutorial/handling-errors/).
\ No newline at end of file
+- For more information on how to handle errors in FASTAPI refer to this [documentation](https://fastapi.tiangolo.com/tutorial/handling-errors/).