fix blocking issues

Author: nibaccam

articles/machine-learning/concept-train-machine-learning-model.md

@@ -64,7 +64,7 @@ Define the iterations, hyperparameter settings, featurization, and other setting
 * [Examples: Jupyter Notebook examples for automated machine learning](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning)
 * [How to: Configure automated ML experiments in Python](how-to-configure-auto-train.md)
 * [How to: Autotrain a time-series forecast model](how-to-auto-train-forecast.md)
-* [How to: Create, explore, and deploy automated machine learning experiments with [Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md)
+* [How to: Create, explore, and deploy automated machine learning experiments with Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md)
 
 ### Estimators
 

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

@@ -169,7 +169,7 @@ Some examples include:
 
 The three different `task` parameter values (the third task-type is `forecasting`, and uses a similar algorithm pool as `regression` tasks) determine the list of models to apply. Use the `whitelist` or `blacklist` parameters to further modify iterations with the available models to include or exclude. The list of supported models can be found on [SupportedModels Class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels) for ([Classification](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification), [Forecasting](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting), and [Regression](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression)).
 
-Automated ML's validation serivce will require that `experiment_timeout_minutes` be set to a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
+Automated ML's validation service will require that `experiment_timeout_minutes` be set to a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
 
 ### Primary Metric
 The primary metric determines the metric to be used during model training for optimization. The available metrics you can select is determined by the task type you choose, and the following table shows valid primary metrics for each task type.