Fix links

Author: cdpark

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

@@ -232,7 +232,7 @@ Named entity recognition (NER)|`"eng"` <br>  `"deu"` <br> `"mul"`|  English&nbsp
 
 [!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
 
-You can specify your dataset language in the featurization section of your configuration YAML file. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in automated ML (SDK v1)](./v1/how-to-configure-auto-features.md#bert-integration-in-automated-ml).
+You can specify your dataset language in the featurization section of your configuration YAML file. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in AutoML (SDK v1)](./v1/how-to-configure-auto-features.md#bert-integration-in-automl).
 
 ```azurecli
 featurization:
@@ -243,7 +243,7 @@ featurization:
 
  [!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
 
-You can specify your dataset language with the `set_featurization()` method. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in automated ML (SDK v1)](./v1/how-to-configure-auto-features.md?view=azureml-api-1&preserve-view=true#bert-integration-in-automated-ml).
+You can specify your dataset language with the `set_featurization()` method. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in AutoML (SDK v1)](./v1/how-to-configure-auto-features.md?view=azureml-api-1&preserve-view=true#bert-integration-in-automl).
 
 ```python
 text_classification_job.set_featurization(dataset_language='eng')

articles/machine-learning/v1/how-to-auto-train-nlp-models.md

@@ -189,7 +189,7 @@ Multi-class text classification|    `'eng'` <br>  `'deu'` <br> `'mul'`|  English
 Named entity recognition (NER)|    `'eng'` <br>  `'deu'` <br> `'mul'`|  English&nbsp;BERT&nbsp;[cased](https://huggingface.co/bert-base-cased) <br>  [German BERT](https://huggingface.co/bert-base-german-cased)<br>  [Multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) <br><br>For all other languages, automated ML applies multilingual BERT
 
 
-You can specify your dataset language in your `FeaturizationConfig`. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in automated ML](how-to-configure-auto-features.md#bert-integration-in-automated-ml).
+You can specify your dataset language in your `FeaturizationConfig`. BERT is also used in the featurization process of automated ML experiment training, learn more about [BERT integration and featurization in AutoML](how-to-configure-auto-features.md#bert-integration-in-automl).
 
 ```python
 from azureml.automl.core.featurization import FeaturizationConfig

articles/machine-learning/v1/how-to-configure-auto-features.md

@@ -143,13 +143,13 @@ Supported customizations include:
 |--|--|
 |**Column purpose update**|Override the autodetected feature type for the specified column.|
 |**Transformer parameter update** |Update the parameters for the specified transformer. Currently supports *Imputer* (mean, most frequent, and median) and *HashOneHotEncoder*.|
-|**Drop columns** |Specifies columns to drop from being featurized.|
+|**Drop columns*** |Specifies columns to drop from being featurized.|
 |**Block transformers**| Specifies block transformers to be used in the featurization process.|
 
 >[!NOTE]
-> The **drop columns** functionality is deprecated as of SDK version 1.19. Drop columns from your dataset as part of data cleansing, before consuming it in your AutoML experiment.
+> *The **drop columns** functionality is deprecated as of SDK version 1.19. Drop columns from your dataset as part of data cleansing, before consuming it in your AutoML experiment.
 
-Create the `FeaturizationConfig` object by using API calls:
+You can create the `FeaturizationConfig` object by using API calls:
 
 ```python
 featurization_config = FeaturizationConfig()
@@ -241,7 +241,7 @@ Output
 
 ### Scaling and normalization
 
-To understand scaling and normalization, and the selected algorithm with its hyperparameter values, use `fitted_model.steps`.
+To understand scaling/normalization and the selected algorithm with its hyperparameter values, use `fitted_model.steps`.
 
 The following sample output is from running `fitted_model.steps` for a chosen run:
 
@@ -334,9 +334,9 @@ If the underlying model doesn't support the `predict_proba()` function or the fo
 
 ## BERT integration in AutoML
 
-[BERT](https://techcommunity.microsoft.com/t5/azure-ai/how-bert-is-integrated-into-azure-automated-machine-learning/ba-p/1194657) is used in the featurization layer of AutoML. In this layer, if a column contains free text or other types of data like timestamps or simple numbers, then featurization is applied accordingly.
+[Bidirectional Encoder Representations from Transformers (BERT)](https://techcommunity.microsoft.com/t5/azure-ai/how-bert-is-integrated-into-azure-automated-machine-learning/ba-p/1194657) is used in the featurization layer of AutoML. In this layer, if a column contains free text or other types of data like timestamps or simple numbers, then featurization is applied accordingly.
 
-For BERT, the model is fine-tuned and trained utilizing the user-provided labels. From here, document embeddings are output as features alongside others, like timestamp-based features, day of week.
+For BERT, the model is fine-tuned and trained by utilizing the user-provided labels. From here, document embeddings are output as features alongside others, like timestamp-based features, day of week.
 
 Learn how to [Set up AutoML to train a natural language processing model with Python](how-to-auto-train-nlp-models.md).
 
@@ -346,7 +346,7 @@ In order to invoke BERT, set `enable_dnn: True` in your `automl_settings` and us
 
 AutoML takes the following steps for BERT.
 
-1. **Preprocesses and tokenizes all text columns**. For example, the `StringCast` transformer can be found in the final model's featurization summary. An example of how to produce the model's featurization summary can be found in [this Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/classification-text-dnn/auto-ml-classification-text-dnn.ipynb).
+1. **Preprocesses and tokenizes all text columns**. For example, the `StringCast` transformer can be found in the final model's featurization summary. An example of how to produce the model's featurization summary can be found in this [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/classification-text-dnn/auto-ml-classification-text-dnn.ipynb).
 
 1. **Concatenates all text columns into a single text column**, hence the `StringConcatTransformer` in the final model.