Docs improvements and redirect fixes (#16287)

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

README.md

@@ -47,7 +47,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 
 ## <div align="center">Documentation</div>
 
-See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com/) for full documentation on training, validation, prediction and deployment.
 
 <details open>
 <summary>Install</summary>
@@ -60,7 +60,7 @@ Pip install the ultralytics package including all [requirements](https://github.
 pip install ultralytics
 ```
 
-For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
+For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart/).
 
 [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
 
@@ -77,7 +77,7 @@ YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` co
 yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
-`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
+`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
 
 ### Python
 
@@ -97,7 +97,7 @@ results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
 path = model.export(format="onnx")  # export the model to ONNX format
 ```
 
-See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.
 
 </details>
 
@@ -116,7 +116,7 @@ Ultralytics provides interactive notebooks for YOLOv8, covering training, valida
 
 ## <div align="center">Models</div>
 
-YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
 
 <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
 
@@ -227,7 +227,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
 
 ## <div align="center">Integrations</div>
 
-Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
+Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
 
 <br>
 <a href="https://ultralytics.com/hub" target="_blank">
@@ -262,7 +262,7 @@ Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub)
 
 ## <div align="center">Contribute</div>
 
-We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
+We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
 
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