warnings fix

Author: shubham soni

articles/machine-learning/how-to-auto-train-image-models.md

@@ -286,9 +286,9 @@ Automatic sweeps can yield competitive results for many datasets. Additionally,
 An AutoML training job uses a primary metric for model optimization and hyperparameter tuning. The primary metric depends on the task type as shown below; other primary metric values are currently not supported. 
 
 * [Accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) for image classification
-* [Intersection over union]((https://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html#sklearn.metrics.jaccard_score)) for image classification multilabel
-* [Mean average precision](en-us/azure/machine-learning/how-to-understand-automated-ml#object-detection-and-instance-segmentation-metrics) for image object detection
-* [Mean average precision](en-us/azure/machine-learning/how-to-understand-automated-ml#object-detection-and-instance-segmentation-metrics) for image instance segmentation
+* [Intersection over union](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html) for image classification multilabel
+* [Mean average precision](how-to-understand-automated-ml#object-detection-and-instance-segmentation-metrics) for image object detection
+* [Mean average precision](how-to-understand-automated-ml#object-detection-and-instance-segmentation-metrics) for image instance segmentation
 
 ### Job limits
 

articles/machine-learning/reference-automl-images-cli-instance-segmentation.md

@@ -31,7 +31,7 @@ For information on all the keys in Yaml syntax, see [Yaml syntax](./reference-au
 | --- | ---- | ----------- | -------------- | ------------- |
 | `task` | const | **Required.** The type of AutoML task. | `image_instance_segmentation` | `image_instance_segmentation` |
 | `primary_metric` | string |  The metric that AutoML will optimize for model selection. |`mean_average_precision` | `mean_average_precision` |
-| `training_parameters` | object | Dictionary containing training parameters for the job. Provide an object that has keys as listed in following sections. <br> - [Model specific hyperparameters](./reference-automl-images-hyperparameters.md#model-specific-hyperparameters) for maskrcnn_* (if you're using maskrcnn_* for instance segmentation) <br> - [Model agnostic hyperparameters](./reference-automl-images-hyperparameters.md#model-agnostic-hyperparameters) <br> - [Object detection and instance segmentation task specific hyperparameters](./reference-automl-images-hyperparameters.md#object-detection-and-instance-segmentation-task-specific-hyperparameters). <br> <br> For an example, see [Supported model algorithms](./how-to-auto-train-image-models.md?tabs=cli#supported-model-algorithms) section.| | |
+| `training_parameters` | object | Dictionary containing training parameters for the job. Provide an object that has keys as listed in following sections. <br> - [Model specific hyperparameters](./reference-automl-images-hyperparameters.md#model-specific-hyperparameters) for maskrcnn_* (if you're using maskrcnn_* for instance segmentation) <br> - [Model agnostic hyperparameters](./reference-automl-images-hyperparameters.md#model-agnostic-hyperparameters) <br> - [Object detection and instance segmentation task specific hyperparameters](./reference-automl-images-hyperparameters.md#object-detection-and-instance-segmentation-task-specific-hyperparameters). <br> <br> For an example, see [Supported model architectures](./how-to-auto-train-image-models.md?tabs=cli#supported-model-architectures) section.| | |
 
 ## Remarks
 

articles/machine-learning/v1/how-to-inference-onnx-automl-image-models-v1.md

@@ -124,7 +124,7 @@ automl_image_run = AutoMLRun(experiment=experiment, run_id=run_id)
 best_child_run = automl_image_run.get_best_child()
 ```
 
-Use the following model specific arguments to submit the script. For more details on arguments, refer to [model specific hyperparameters](reference-automl-images-hyperparameters-v1.md#model-specific-hyperparameters) and for supported object detection model names refer to the [supported model algorithm section](how-to-auto-train-image-models-v1.md#supported-model-algorithms).
+Use the following model specific arguments to submit the script. For more details on arguments, refer to [model specific hyperparameters](reference-automl-images-hyperparameters-v1.md#model-specific-hyperparameters) and for supported object detection model names refer to the [supported model architectures section](how-to-auto-train-image-models-v1.md#supported-model-architectures).
 
 To get the argument values needed to create the batch scoring model, refer to the scoring scripts generated under the outputs folder of the AutoML training runs. Use the hyperparameter values available in the model settings variable inside the scoring file for the best child run.
 
@@ -713,7 +713,7 @@ assert batch_size == img_data.shape[0]
 
 # [Object detection with Faster R-CNN or RetinaNet](#tab/object-detect-cnn)
 
-For object detection with the Faster R-CNN algorithm, follow the same preprocessing steps as image classification, except for image cropping. You can resize the image with height `600` and width `800`. You can get the expected input height and width with the following code.
+For object detection with the Faster R-CNN architecture, follow the same preprocessing steps as image classification, except for image cropping. You can resize the image with height `600` and width `800`. You can get the expected input height and width with the following code.
 
 ```python
 batch, channel, height_onnx, width_onnx = session.get_inputs()[0].shape
@@ -776,7 +776,7 @@ assert batch_size == img_data.shape[0]
 
 # [Object detection with YOLO](#tab/object-detect-yolo)
 
-For object detection with the YOLO algorithm, follow the same preprocessing steps as image classification, except for image cropping. You can resize the image with height `600` and width `800`, and get the expected input height and width with the following code.
+For object detection with the YOLO architecture, follow the same preprocessing steps as image classification, except for image cropping. You can resize the image with height `600` and width `800`, and get the expected input height and width with the following code.
 
 ```python
 batch, channel, height_onnx, width_onnx = session.get_inputs()[0].shape