Merge pull request #108292 from likebupt/update-0319

correct typo

Author: PRMerger20

articles/machine-learning/algorithm-module-reference/edit-metadata.md

@@ -15,7 +15,7 @@ ms.date: 02/11/2020
 
 This article describes a module included in Azure Machine Learning designer (preview).
 
-Use the Edit Data module to change metadata that's associated with columns in a dataset. The value and data type of the dataset will  change after use of the Edit Metadata module.
+Use the Edit Metadata module to change metadata that's associated with columns in a dataset. The value and data type of the dataset will  change after use of the Edit Metadata module.
 
 Typical metadata changes might include:
 

articles/machine-learning/algorithm-module-reference/tune-model-hyperparameters.md

@@ -15,7 +15,7 @@ ms.date: 02/11/2020
 
 This article describes how to use the Tune Model Hyperparameters module in Azure Machine Learning designer (preview). The goal is to determine the optimum hyperparameters for a machine learning model. The module builds and tests multiple models by using different combinations of settings. It compares metrics over all models to get the combinations of settings. 
 
-The terms *parameter* and *hyperparameter* can be confusing. The model's *parameters* are what you set in the properties pane. Basically, this module performs a *parameter sweep* over the specified parameter settings. It learns an optimal set of _hyperparameters_, which might be different for each specific decision tree, dataset, or regression method. The process of finding the optimal configuration is sometimes called *tuning*. 
+The terms *parameter* and *hyperparameter* can be confusing. The model's *parameters* are what you set in the right pane of the module. Basically, this module performs a *parameter sweep* over the specified parameter settings. It learns an optimal set of _hyperparameters_, which might be different for each specific decision tree, dataset, or regression method. The process of finding the optimal configuration is sometimes called *tuning*. 
 
 The module supports the following method for finding the optimum settings for a model: *integrated train and tune.* In this method, you configure a set of parameters to use. You then let the module iterate over multiple combinations. The module measures accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
 

articles/machine-learning/how-to-debug-pipelines.md

@@ -28,7 +28,7 @@ The following sections provide an overview of common pitfalls when building pipe
 
 One of the most common failures in a pipeline is that an attached script (data cleansing script, scoring script, etc.) is not running as intended, or contains runtime errors in the remote compute context that are difficult to debug in your workspace in the Azure Machine Learning studio. 
 
-Pipelines themselves cannot be run locally, but running the scripts in isolation on your local machine allows you to debug faster because you don’t have to wait for the compute and environment build process. Some development work is required to do this:
+Pipelines themselves cannot be run locally, but running the scripts in isolation on your local machine allows you to debug faster because you don't have to wait for the compute and environment build process. Some development work is required to do this:
 
 * If your data is in a cloud datastore, you will need to download data and make it available to your script. Using a small sample of your data is a good way to cut down on runtime and quickly get feedback on script behavior
 * If you are attempting to simulate an intermediate pipeline step, you may need to manually build the object types that the particular script is expecting from the prior step
@@ -133,7 +133,7 @@ For pipelines created in the designer, you can find the **log files** on either
 When you submit a pipeline run and stay in the authoring page, you can find the log files generated for each module.
 
 1. Select any module in the authoring canvas.
-1. In the properties pane, go to the  **Logs** tab.
+1. In the right pane of the module, go to the  **Outputs+ogs** tab.
 1. Select the log file `70_driver_log.txt`
 
     ![Authoring page module logs](./media/how-to-debug-pipelines/pipelinerun-05.png)
@@ -145,7 +145,7 @@ You can also find the log files of specific runs in the pipeline run detail page
 1. Select a pipeline run created in the designer.
     ![Pipeline run page](./media/how-to-debug-pipelines/pipelinerun-04.png)
 1. Select any module in the preview pane.
-1. In the properties pane, go to the **Logs** tab.
+1. In the right pane of the module, go to the  **Outputs+ogs** tab.
 1. Select the log file `70_driver_log.txt`
 
 ## Debug and troubleshoot in Application Insights