Add FAQ sections to Modes and Tasks (#14181)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>

docs/en/usage/python.md

@@ -330,3 +330,89 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
 You can easily customize Trainers to support custom tasks or explore R&D ideas. Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.
 
 [Customization tutorials](engine.md){ .md-button }
+
+## FAQ
+
+### How can I integrate YOLOv8 into my Python project for object detection?
+
+Integrating Ultralytics YOLOv8 into your Python projects is simple. You can load a pre-trained model or train a new model from scratch. Here's how to get started:
+
+```python
+from ultralytics import YOLO
+
+# Load a pretrained YOLO model
+model = YOLO("yolov8n.pt")
+
+# Perform object detection on an image
+results = model("https://ultralytics.com/images/bus.jpg")
+
+# Visualize the results
+for result in results:
+    result.show()
+```
+
+See more detailed examples in our [Predict Mode](../modes/predict.md) section.
+
+### What are the different modes available in YOLOv8?
+
+Ultralytics YOLOv8 provides various modes to cater to different machine learning workflows. These include:
+
+- **[Train](../modes/train.md)**: Train a model using custom datasets.
+- **[Val](../modes/val.md)**: Validate model performance on a validation set.
+- **[Predict](../modes/predict.md)**: Make predictions on new images or video streams.
+- **[Export](../modes/export.md)**: Export models to various formats like ONNX, TensorRT.
+- **[Track](../modes/track.md)**: Real-time object tracking in video streams.
+- **[Benchmark](../modes/benchmark.md)**: Benchmark model performance across different configurations.
+
+Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.
+
+### How do I train a custom YOLOv8 model using my dataset?
+
+To train a custom YOLOv8 model, you need to specify your dataset and other hyperparameters. Here's a quick example:
+
+```python
+from ultralytics import YOLO
+
+# Load the YOLO model
+model = YOLO("yolov8n.yaml")
+
+# Train the model with custom dataset
+model.train(data="path/to/your/dataset.yaml", epochs=10)
+```
+
+For more details on training and hyperlinks to example usage, visit our [Train Mode](../modes/train.md) page.
+
+### How do I export YOLOv8 models for deployment?
+
+Exporting YOLOv8 models in a format suitable for deployment is straightforward with the `export` function. For example, you can export a model to ONNX format:
+
+```python
+from ultralytics import YOLO
+
+# Load the YOLO model
+model = YOLO("yolov8n.pt")
+
+# Export the model to ONNX format
+model.export(format="onnx")
+```
+
+For various export options, refer to the [Export Mode](../modes/export.md) documentation.
+
+### Can I validate my YOLOv8 model on different datasets?
+
+Yes, validating YOLOv8 models on different datasets is possible. After training, you can use the validation mode to evaluate the performance:
+
+```python
+from ultralytics import YOLO
+
+# Load a YOLOv8 model
+model = YOLO("yolov8n.yaml")
+
+# Train the model
+model.train(data="coco8.yaml", epochs=5)
+
+# Validate the model on a different dataset
+model.val(data="path/to/separate/data.yaml")
+```
+
+Check the [Val Mode](../modes/val.md) page for detailed examples and usage.