Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/integrations/coreml.md

@@ -6,9 +6,9 @@ keywords: CoreML export, YOLOv8 models, CoreML conversion, Ultralytics, iOS obje
 
 # CoreML Export for YOLOv8 Models
 
-Deploying computer vision models on Apple devices like iPhones and Macs requires a format that ensures seamless performance.
+Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on Apple devices like iPhones and Macs requires a format that ensures seamless performance.
 
-The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for efficient object detection in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
+The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for efficient [object detection](https://www.ultralytics.com/glossary/object-detection) in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
 
 ## CoreML
 
@@ -16,7 +16,7 @@ The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https:
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/coreml-overview.avif" alt="CoreML Overview">
 </p>
 
-[CoreML](https://developer.apple.com/documentation/coreml) is Apple's foundational machine learning framework that builds upon Accelerate, BNNS, and Metal Performance Shaders. It provides a machine-learning model format that seamlessly integrates into iOS applications and supports tasks such as image analysis, natural language processing, audio-to-text conversion, and sound analysis.
+[CoreML](https://developer.apple.com/documentation/coreml) is Apple's foundational machine learning framework that builds upon Accelerate, BNNS, and Metal Performance Shaders. It provides a machine-learning model format that seamlessly integrates into iOS applications and supports tasks such as image analysis, [natural language processing](https://www.ultralytics.com/glossary/natural-language-processing-nlp), audio-to-text conversion, and sound analysis.
 
 Applications can take advantage of Core ML without the need to have a network connection or API calls because the Core ML framework works using on-device computing. This means model inference can be performed locally on the user's device.
 
@@ -24,15 +24,15 @@ Applications can take advantage of Core ML without the need to have a network co
 
 Apple's CoreML framework offers robust features for on-device machine learning. Here are the key features that make CoreML a powerful tool for developers:
 
-- **Comprehensive Model Support**: Converts and runs models from popular frameworks like TensorFlow, PyTorch, scikit-learn, XGBoost, and LibSVM.
+- **Comprehensive Model Support**: Converts and runs models from popular frameworks like TensorFlow, [PyTorch](https://www.ultralytics.com/glossary/pytorch), scikit-learn, XGBoost, and LibSVM.
 
 <p align="center">
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/coreml-supported-models.avif" alt="CoreML Supported Models">
 </p>
 
-- **On-device Machine Learning**: Ensures data privacy and swift processing by executing models directly on the user's device, eliminating the need for network connectivity.
+- **On-device [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml)**: Ensures data privacy and swift processing by executing models directly on the user's device, eliminating the need for network connectivity.
 
-- **Performance and Optimization**: Uses the device's CPU, GPU, and Neural Engine for optimal performance with minimal power and memory usage. Offers tools for model compression and optimization while maintaining accuracy.
+- **Performance and Optimization**: Uses the device's CPU, GPU, and Neural Engine for optimal performance with minimal power and memory usage. Offers tools for model compression and optimization while maintaining [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 - **Ease of Integration**: Provides a unified format for various model types and a user-friendly API for seamless integration into apps. Supports domain-specific tasks through frameworks like Vision and Natural Language.
 
@@ -111,7 +111,7 @@ For more details about the export process, visit the [Ultralytics documentation
 
 Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next critical phase is deploying these models effectively. For detailed guidance on deploying CoreML models in various environments, check out these resources:
 
-- **[CoreML Tools](https://apple.github.io/coremltools/docs-guides/)**: This guide includes instructions and examples to convert models from TensorFlow, PyTorch, and other libraries to Core ML.
+- **[CoreML Tools](https://apple.github.io/coremltools/docs-guides/)**: This guide includes instructions and examples to convert models from [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), PyTorch, and other libraries to Core ML.
 
 - **[ML and Vision](https://developer.apple.com/videos/)**: A collection of comprehensive videos that cover various aspects of using and implementing CoreML models.
 
@@ -164,7 +164,7 @@ For further details, refer to the [Exporting YOLOv8 Models to CoreML](../modes/e
 
 CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models on Apple devices:
 
-- **On-device Processing**: Enables local model inference on devices, ensuring data privacy and minimizing latency.
+- **On-device Processing**: Enables local model inference on devices, ensuring [data privacy](https://www.ultralytics.com/glossary/data-privacy) and minimizing latency.
 - **Performance Optimization**: Leverages the full potential of the device's CPU, GPU, and Neural Engine, optimizing both speed and efficiency.
 - **Ease of Integration**: Offers a seamless integration experience with Apple's ecosystems, including iOS, macOS, watchOS, and tvOS.
 - **Versatility**: Supports a wide range of machine learning tasks such as image analysis, audio processing, and natural language processing using the CoreML framework.