Pm feedback

Author: nibaccam

articles/machine-learning/concept-automated-ml.md

@@ -98,7 +98,7 @@ Enable this setting with:
 
 + Azure Machine Learning studio: Enable **Automatic featurization** in the **View additional configuration** section [with these steps](how-to-create-portal-experiments.md#create-and-run-experiment).
 
-+ Python SDK: Specifying `"feauturization": auto' / 'off' / FeaturizationConfig` for the [`AutoMLConfig` class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig). 
++ Python SDK: Specifying `"feauturization": 'auto' / 'off' / 'FeaturizationConfig'` for the [`AutoMLConfig` class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig). 
 
 ## Prevent over-fitting
 

articles/machine-learning/how-to-configure-auto-train.md

@@ -190,9 +190,9 @@ When configuring your experiments, you can enable the advanced setting `feauturi
 
 |Featurization Configuration | Description |
 | ------------- | ------------- |
-|"feauturization = 'FeaturizationConfig'"| Indicates customized featurization step should be used. [Learn how to customize featurization](how-to-configure-auto-train.md#customize-feature-engineering).|
-|"feauturization" = 'off'| Indicates featurization step should not be done automatically.|
-|"feauturization" = 'auto'| Indicates that as part of preprocessing, [data guardrails and featurization steps](how-to-create-portal-experiments.md#advanced-featurization-options) are performed automatically.|
+|`"feauturization":` `'FeaturizationConfig'`| Indicates customized featurization step should be used. [Learn how to customize featurization](how-to-configure-auto-train.md#customize-feature-engineering).|
+|`"feauturization": 'off'`| Indicates featurization step should not be done automatically.|
+|`"feauturization": 'auto'`| Indicates that as part of preprocessing, [data guardrails and featurization steps](how-to-create-portal-experiments.md#advanced-featurization-options) are performed automatically.|
 
 > [!NOTE]
 > Automated machine learning featurization steps (feature normalization, handling missing data,