Merge pull request #107642 from PeterCLu/edit-pass-plu

Minor style tweaks

Author: PRMerger9

articles/machine-learning/algorithm-module-reference/create-python-model.md

@@ -28,7 +28,7 @@ After you create the model, you can use [Train Model](train-model.md) to train t
 Use of this module requires intermediate or expert knowledge of Python. The module supports use of any learner that's included in the Python packages already installed in Azure Machine Learning. See the preinstalled Python package list in [Execute Python Script](execute-python-script.md).
 
 
-This article shows how to use Create Python Model with a simple pipeline. Here's a diagram of the pipeline:
+This article shows how to use **Create Python Model** with a simple pipeline. Here's a diagram of the pipeline:
 
 ![Diagram of Create Python Model](./media/module/create-python-model.png)
 
@@ -68,7 +68,7 @@ This article shows how to use Create Python Model with a simple pipeline. Here's
 
    ```
 
-1. Connect the Create Python Model module that you just created to Train Model and Score Model.
+1. Connect the **Create Python Model** module that you just created to **Train Model** and **Score Model**.
 
 1. If you need to evaluate the model, add an [Execute Python Script](execute-python-script.md) module and edit the Python script.
 

articles/machine-learning/algorithm-module-reference/enter-data-manually.md

@@ -15,7 +15,7 @@ ms.date: 02/22/2020
 
 This article describes a module in Azure Machine Learning designer (preview).
 
-Use the Enter Data Manually module to create a small dataset by typing values. The dataset can have multiple columns.
+Use the **Enter Data Manually** module to create a small dataset by typing values. The dataset can have multiple columns.
 
 This module can be helpful in scenarios such as:  
 

articles/machine-learning/algorithm-module-reference/partition-and-sample.md

@@ -70,7 +70,7 @@ This option supports simple random sampling or stratified random sampling. It's
 
 1. **Rate of sampling**: Enter a value between 0 and 1. this value specifies the percentage of rows from the source dataset that should be included in the output dataset.
 
-   For example, if you want only half of the original dataset, enter **0.5** to indicate that the sampling rate should be 50 percent.
+   For example, if you want only half of the original dataset, enter `0.5` to indicate that the sampling rate should be 50 percent.
 
    The rows of the input dataset are shuffled and selectively placed in the output dataset, according to the specified ratio.
 
@@ -149,7 +149,7 @@ Use this option when you have divided a dataset into multiple partitions and now
 
 1. If you're working with multiple partitions, you must add more instances of the **Partition and Sample** module to handle each partition.
 
-   For example, the **Partition and Sample** module in the second row is set to **Assign to folds**, and the module in the third row is set to **Pick fold**.   
+   For example, the **Partition and Sample** module in the second row is set to **Assign to Folds**, and the module in the third row is set to **Pick Fold**.   
 
    ![Partition and sample](./media/module/partition-and-sample.png)
 

articles/machine-learning/algorithm-module-reference/split-data.md

@@ -29,21 +29,21 @@ This module is useful when you need to separate data into training and testing s
 
 1. **Splitting mode**: Choose one of the following modes, depending on the type of data you have and how you want to divide it. Each splitting mode has different options.
 
-   - **Split rows**: Use this option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split. By default, the data is divided 50/50.
+   - **Split Rows**: Use this option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split. By default, the data is divided 50/50.
 
      You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.  
 
-   - **Regular expression split**: Choose this option when you want to divide your dataset by testing a single column for a value.
+   - **Regular Expression Split**: Choose this option when you want to divide your dataset by testing a single column for a value.
 
      For example, if you're analyzing sentiment, you can check for the presence of a particular product name in a text field. You can then divide the dataset into rows with the target product name and rows  without the target product name.
 
-   - **Relative expression split**: Use this option whenever you want to apply a condition to a number column. The number can be a date/time field, a column that contains age or dollar amounts, or even a percentage. For example, you might want to divide your dataset based on the cost of the items, group people by age ranges, or separate data by a calendar date.
+   - **Relative Expression Split**: Use this option whenever you want to apply a condition to a number column. The number can be a date/time field, a column that contains age or dollar amounts, or even a percentage. For example, you might want to divide your dataset based on the cost of the items, group people by age ranges, or separate data by a calendar date.
 
 ### Split rows
 
 1. Add the [Split Data](./split-data.md) module to your pipeline in the designer, and connect the dataset that you want to split.
 
-1. For **Splitting mode**, select **Split rows**. 
+1. For **Splitting mode**, select **Split Rows**. 
 
 1. **Fraction of rows in the first output dataset**: Use this option to determine how many rows will go into the first (left side) output. All other rows will go into the second (right side) output.
 
@@ -102,7 +102,7 @@ The first result dataset contains all rows where the index column begins with on
 
 1. Add the [Split Data](./split-data.md) module to your pipeline, and connect it as input to the dataset that you want to split.
 
-1. For **Splitting mode**, select **Relative expression split**.
+1. For **Splitting mode**, select **Relative Expression**.
 
 1. In the **Relational expression** box, enter an expression that performs a comparison operation on a single column.
 
@@ -120,7 +120,7 @@ The first result dataset contains all rows where the index column begins with on
 
    The expression divides the dataset into two sets of rows: rows with values that meet the condition, and all remaining rows.
 
-The following examples demonstrate how to divide a dataset by using the **Relative expression** option in the **Split Data** module.  
+The following examples demonstrate how to divide a dataset by using the **Relative Expression** option in the **Split Data** module.  
 
 ### Calendar year
 

articles/machine-learning/how-to-retrain-designer.md

@@ -26,7 +26,7 @@ In this article, you learn how to:
 
 ## Prerequisites
 
-* An Azure subscription. If you don’t have an Azure subscription, create a [free account](https://aka.ms/AMLFree).
+* An Azure subscription. If you don't have an Azure subscription, create a [free account](https://aka.ms/AMLFree).
 * An Azure Machine Learning workspace with the Enterprise SKU.
 
 This article assumes that you have basic knowledge of building pipelines in the designer. For a guided introduction to the designer, complete the [tutorial](tutorial-designer-automobile-price-train-score.md). 
@@ -70,7 +70,7 @@ The designer saves all pipeline outputs, including trained models, to the defaul
 Add pipeline parameters to dynamically set variables at runtime. For this pipeline, add a parameter for the training data path so that you can retrain your model on a new dataset.
 
 1. Select the **Import Data** module.
-1. On the settings pane, select the ellipses above the **Path** field.
+1. In the settings pane, select the ellipses above the **Path** field.
 1. Select  **Add to pipeline parameter**.
 1. Provide a parameter name and a default value.