diff --git a/docs/en/index.md b/docs/en/index.md
index 7706120a..91b8f87b 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -32,7 +32,7 @@ keywords: Ultralytics, YOLO, YOLO11, object detection, image segmentation, deep
     <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
 </div>
 
-Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
+Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/blog/everything-you-need-to-know-about-computer-vision-in-2025), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
 
 Explore the Ultralytics Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLO's potential in your projects
 
@@ -87,7 +87,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
 
     [:octicons-arrow-right-24: Learn more](modes/train.md)
 
-- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore Tasks**
+- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore computer vision tasks**
 
     ***
 
@@ -115,7 +115,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
 
     ***
 
-    [:octicons-arrow-right-24: License](https://www.ultralytics.com/license)
+    [:octicons-arrow-right-24: YOLO License](https://www.ultralytics.com/license)
 
 </div>
 
@@ -143,7 +143,7 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
 - [YOLOv8](https://github.com/ultralytics/ultralytics) released in 2023 by Ultralytics. YOLOv8 introduced new features and improvements for enhanced performance, flexibility, and efficiency, supporting a full range of vision AI tasks,
 - [YOLOv9](models/yolov9.md) introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
 - [YOLOv10](models/yolov10.md) is created by researchers from [Tsinghua University](https://www.tsinghua.edu.cn/en/) using the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/). This version provides real-time [object detection](tasks/detect.md) advancements by introducing an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements.
-- **[YOLO11](models/yolo11.md) 🚀 NEW**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md), leverage capabilities across diverse AI applications and domains.
+- **[YOLO11](models/yolo11.md) 🚀 NEW**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks, including [object detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md), leverage capabilities across diverse AI applications and domains.
 
 ## YOLO Licenses: How is Ultralytics YOLO licensed?