diff --git a/docs/en/index.md b/docs/en/index.md
index 8217f972..c0478d70 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -54,11 +54,69 @@ Explore the Ultralytics Docs, a comprehensive resource designed to help you unde
 
 ## Where to Start
 
-- **Install** `ultralytics` with pip and get up and running in minutes   [:material-clock-fast: Get Started](quickstart.md){ .md-button }
-- **Predict** new images and videos with YOLO   [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
-- **Train** a new YOLO model on your own custom dataset   [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
-- **Tasks** YOLO tasks like segment, classify, pose and track   [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
-- **[YOLO11](models/yolo11.md) 🚀 NEW**: Ultralytics' latest SOTA models   [:material-magnify-expand: Explore new YOLO11 models](models/yolo11.md){ .md-button }
+<div class="grid cards" markdown>
+
+- :material-clock-fast:{ .lg .middle } &nbsp; **Getting Started**
+
+    ***
+
+    Install `ultralytics` with pip and get up and running in minutes to train a YOLO model
+
+    ***
+
+    [:octicons-arrow-right-24: Quickstart](quickstart.md)
+
+- :material-image:{ .lg .middle } &nbsp; **Predict**
+
+    ***
+
+    Predict on new images, videos and streams with YOLO <br />
+
+    ***
+
+    [:octicons-arrow-right-24: Learn more](modes/predict.md)
+
+- :fontawesome-solid-brain:{ .lg .middle } &nbsp; **Train a Model**
+
+    ***
+
+    Train a new YOLO model on your own custom dataset from scratch or load and train on a pretrained model
+
+    ***
+
+    [:octicons-arrow-right-24: Learn more](modes/train.md)
+
+- :material-magnify-expand:{ .lg .middle } &nbsp; **Explore Tasks**
+
+    ***
+
+    Discover YOLO tasks like detect, segment, classify, pose, OBB and track <br />
+
+    ***
+
+    [:octicons-arrow-right-24: Explore Tasks](tasks/index.md)
+
+- :rocket:{ .lg .middle } &nbsp; **Explore YOLO11 NEW**
+
+    ***
+
+    Discover Ultralytics' latest state-of-the-art YOLO11 models and their capabilities <br />
+
+    ***
+
+    [:octicons-arrow-right-24: YOLO11 Models 🚀 NEW](models/yolo11.md)
+
+- :material-scale-balance:{ .lg .middle } &nbsp; **Open Source, AGPL-3.0**
+
+    ***
+
+    Ultralytics offers two licensing options for YOLO: AGPL-3.0 License and Enterprise License. Ultralytics is available on [GitHub](https://github.com/ultralytics/ultralytics)
+
+    ***
+
+    [:octicons-arrow-right-24: License](https://www.ultralytics.com/license)
+
+</div>
 
 <p align="center">
   <br>
@@ -105,7 +163,7 @@ Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look
 
 Getting started with YOLO is quick and straightforward. You can install the Ultralytics package using [pip](https://pypi.org/project/ultralytics/) and get up and running in minutes. Here's a basic installation command:
 
-!!! example
+!!! example "Installation using pip"
 
     === "CLI"
 
@@ -121,11 +179,11 @@ Training a custom YOLO model on your dataset involves a few detailed steps:
 
 1. Prepare your annotated dataset.
 2. Configure the training parameters in a YAML file.
-3. Use the `yolo train` command to start training.
+3. Use the `yolo TASK train` command to start training. (Each `TASK` has its own argument)
 
-Here's example code:
+Here's example code for the Object Detection Task:
 
-!!! example
+!!! example "Train Example for Object Detection Task"
 
     === "Python"
 
@@ -143,7 +201,7 @@ Here's example code:
 
         ```bash
         # Train a YOLO model from the command line
-        yolo train data=path/to/dataset.yaml epochs=100 imgsz=640
+        yolo detect train data=path/to/dataset.yaml epochs=100 imgsz=640
         ```
 
 For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@@ -161,7 +219,7 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
 
 Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
 
-!!! example
+!!! example "Example for Object Tracking on a Video"
 
     === "Python"