Embed videos in Docs pages using Parent pages for relevant segments (#6877)

docs/en/usage/callbacks.md

@@ -8,6 +8,17 @@ keywords: Ultralytics, YOLO, callbacks guide, training callback, validation call
 
 Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes. Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of these objects can be found in Reference section of the docs.
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=67"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: Callbacks
+</p>
+
 ## Examples
 
 ### Returning additional information with Prediction

docs/en/usage/cfg.md

@@ -6,6 +6,17 @@ keywords: YOLOv8, settings, hyperparameters, YOLO CLI commands, YOLO tasks, YOLO
 
 YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings and hyperparameters can affect the model's behavior at various stages of the model development process, including training, validation, and prediction.
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=87"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: Configuration
+</p>
+
 Ultralytics commands use the following syntax:
 
 !!! Example

docs/en/usage/cli.md

@@ -10,13 +10,13 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
 
 <p align="center">
   <br>
-  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc"
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=19"
     title="YouTube video player" frameborder="0"
     allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: CLI & Python Usage and Live Inference
+  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: CLI
 </p>
 
 !!! Example

docs/en/usage/engine.md

@@ -6,6 +6,17 @@ keywords: Ultralytics, YOLO, trainer engines, BaseTrainer, DetectionTrainer, cus
 
 Both the Ultralytics YOLO command-line and Python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=104"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: Advanced Customization
+</p>
+
 ## BaseTrainer
 
 BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding the required functions or operations as long the as correct formats are followed. For example, you can support your own custom model and dataloader by just overriding these functions:

docs/en/usage/python.md

@@ -8,6 +8,17 @@ keywords: YOLOv8, Ultralytics, Python, object detection, segmentation, classific
 
 Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=58"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: Python
+</p>
+
 For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code.
 
 !!! Example "Python"