fix: Docs Authoring Markdownlint rules (#20652)

* fix: Mallformed nameWithType refs

* fix: Escape asterisks in xref

* fix: Escape the backtick character in xref links

Author: nschonni

docs/architecture/microservices/implement-resilient-applications/use-httpclientfactory-to-implement-resilient-http-requests.md

@@ -59,9 +59,9 @@ The following diagram shows how Typed Clients are used with `IHttpClientFactory`
 
 **Figure 8-4**. Using `IHttpClientFactory` with Typed Client classes.
 
-In the above image, a `ClientService` (used by a controller or client code) uses an `HttpClient` created by the registered `IHttpClientFactory`. This factory assigns an `HttpMessageHandler` from a pool to the `HttpClient`. The `HttpClient` can be configured with Polly's policies when registering the `IHttpClientFactory` in the DI container with the extension method <xref:Microsoft.Extensions.DependencyInjection.HttpClientFactoryServiceCollectionExtensions.AddHttpClient*>.
+In the above image, a `ClientService` (used by a controller or client code) uses an `HttpClient` created by the registered `IHttpClientFactory`. This factory assigns an `HttpMessageHandler` from a pool to the `HttpClient`. The `HttpClient` can be configured with Polly's policies when registering the `IHttpClientFactory` in the DI container with the extension method <xref:Microsoft.Extensions.DependencyInjection.HttpClientFactoryServiceCollectionExtensions.AddHttpClient%2A>.
 
-To configure the above structure, add <xref:System.Net.Http.IHttpClientFactory> in your application by installing the `Microsoft.Extensions.Http` NuGet package that includes the <xref:Microsoft.Extensions.DependencyInjection.HttpClientFactoryServiceCollectionExtensions.AddHttpClient*> extension method for <xref:Microsoft.Extensions.DependencyInjection.IServiceCollection>. This extension method registers the internal `DefaultHttpClientFactory` class to be used as a singleton for the interface `IHttpClientFactory`. It defines a transient configuration for the <xref:Microsoft.Extensions.Http.HttpMessageHandlerBuilder>. This message handler (<xref:System.Net.Http.HttpMessageHandler> object), taken from a pool, is used by the `HttpClient` returned from the factory.
+To configure the above structure, add <xref:System.Net.Http.IHttpClientFactory> in your application by installing the `Microsoft.Extensions.Http` NuGet package that includes the <xref:Microsoft.Extensions.DependencyInjection.HttpClientFactoryServiceCollectionExtensions.AddHttpClient%2A> extension method for <xref:Microsoft.Extensions.DependencyInjection.IServiceCollection>. This extension method registers the internal `DefaultHttpClientFactory` class to be used as a singleton for the interface `IHttpClientFactory`. It defines a transient configuration for the <xref:Microsoft.Extensions.Http.HttpMessageHandlerBuilder>. This message handler (<xref:System.Net.Http.HttpMessageHandler> object), taken from a pool, is used by the `HttpClient` returned from the factory.
 
 In the next code, you can see how `AddHttpClient()` can be used to register Typed Clients (Service Agents) that need to use `HttpClient`.
 

docs/framework/whats-new/whats-new-in-accessibility.md

@@ -382,7 +382,7 @@ Starting with .NET Framework 4.7.2, the <xref:System.Windows.Forms.DataGridView>
 
 **Changes to the CheckBox and RadioButton controls**
 
-In .NET Framework 4.7.1 and earlier versions, the WPF <xref:System.Windows.Controls.CheckBox?displayProperty=nameWIthType> and <xref:System.Windows.Controls.RadioButton?displayProperty=nameWIthType> controls have inconsistent and, in Classic and High Contrast themes,
+In .NET Framework 4.7.1 and earlier versions, the WPF <xref:System.Windows.Controls.CheckBox?displayProperty=nameWithType> and <xref:System.Windows.Controls.RadioButton?displayProperty=nameWithType> controls have inconsistent and, in Classic and High Contrast themes,
 incorrect focus visuals.  These issues occur in cases where the controls do not have any content set.  This can make the transition between themes confusing and the focus visual hard to see.
 
 In .NET Framework 4.7.2, these visuals are now more consistent across themes and more easily visible in Classic and High Contrast themes.

docs/machine-learning/how-does-mldotnet-work.md

@@ -258,7 +258,7 @@ All algorithms also create new columns after they have performed a prediction. T
 
 You can find out more about output columns of different machine learning tasks in the [Machine Learning Tasks](resources/tasks.md) guide.
 
-An important property of DataView objects is that they are evaluated **lazily**. Data views are only loaded and operated on during model training and evaluation, and data prediction. While you are writing and testing your ML.NET application, you can use the Visual Studio debugger to take a peek at any data view object by calling the [Preview](xref:Microsoft.ML.DebuggerExtensions.Preview*) method.
+An important property of DataView objects is that they are evaluated **lazily**. Data views are only loaded and operated on during model training and evaluation, and data prediction. While you are writing and testing your ML.NET application, you can use the Visual Studio debugger to take a peek at any data view object by calling the [Preview](xref:Microsoft.ML.DebuggerExtensions.Preview%2A) method.
 
 ```csharp
     var debug = testPriceDataView.Preview();

docs/machine-learning/how-to-choose-an-ml-net-algorithm.md

@@ -29,7 +29,7 @@ Before training with a linear algorithm, the features should be normalized. This
 
 In general linear algorithms are scalable and fast, cheap to train, cheap to predict. They scale by the number of features and approximately by the size of the training data set.
 
-Linear algorithms make multiple passes over the training data. If your dataset fits into memory, then adding a [cache checkpoint](xref:Microsoft.ML.LearningPipelineExtensions.AppendCacheCheckpoint*) to your ML.NET pipeline before appending the trainer, will make the training run faster.
+Linear algorithms make multiple passes over the training data. If your dataset fits into memory, then adding a [cache checkpoint](xref:Microsoft.ML.LearningPipelineExtensions.AppendCacheCheckpoint%2A) to your ML.NET pipeline before appending the trainer, will make the training run faster.
 
 **Linear Trainers**
 

docs/machine-learning/how-to-guides/inspect-intermediate-data-ml-net.md

@@ -58,7 +58,7 @@ HousingData[] housingData = new HousingData[]
 
 ## Convert IDataView to IEnumerable
 
-One of the quickest ways to inspect an [`IDataView`](xref:Microsoft.ML.IDataView) is to convert it to an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601). To convert an [`IDataView`](xref:Microsoft.ML.IDataView) to [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) use the [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable*) method.
+One of the quickest ways to inspect an [`IDataView`](xref:Microsoft.ML.IDataView) is to convert it to an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601). To convert an [`IDataView`](xref:Microsoft.ML.IDataView) to [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) use the [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable%2A) method.
 
 To optimize performance, set `reuseRowObject` to `true`. Doing so will lazily populate the same object with the data of the current row as it's being evaluated as opposed to creating a new object for each row in the dataset.
 
@@ -77,10 +77,10 @@ foreach (HousingData row in housingDataEnumerable)
 
 ## Accessing specific indices with IEnumerable
 
-If you only need access to a portion of the data or specific indices, use [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable*) and set the `reuseRowObject` parameter value to `false` so a new object is created for each of the requested rows in the dataset. Then, convert the [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) to an array or list.
+If you only need access to a portion of the data or specific indices, use [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable%2A) and set the `reuseRowObject` parameter value to `false` so a new object is created for each of the requested rows in the dataset. Then, convert the [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) to an array or list.
 
 > [!WARNING]
-> Converting the result of [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable*) to an array or list will load all the requested [`IDataView`](xref:Microsoft.ML.IDataView) rows into memory which may affect performance.
+> Converting the result of [`CreateEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.CreateEnumerable%2A) to an array or list will load all the requested [`IDataView`](xref:Microsoft.ML.IDataView) rows into memory which may affect performance.
 
 Once the collection has been created, you can perform operations on the data. The code snippet below takes the first three rows in the dataset and calculates the average current price.
 
@@ -100,15 +100,15 @@ float averageCurrentPrice = (firstRow.CurrentPrice + secondRow.CurrentPrice + th
 
 ## Inspect values in a single column
 
-At any point in the model building process, values in a single column of an [`IDataView`](xref:Microsoft.ML.IDataView) can be accessed using the [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn*) method. The [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn*) method returns all of the values in a single column as an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601).
+At any point in the model building process, values in a single column of an [`IDataView`](xref:Microsoft.ML.IDataView) can be accessed using the [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn%2A) method. The [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn%2A) method returns all of the values in a single column as an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601).
 
 ```csharp
 IEnumerable<float> sizeColumn = data.GetColumn<float>("Size").ToList();
 ```
 
 ## Inspect IDataView values one row at a time
 
-[`IDataView`](xref:Microsoft.ML.IDataView) is lazily evaluated. To iterate over the rows of an [`IDataView`](xref:Microsoft.ML.IDataView) without converting to an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) as demonstrated in previous sections of this document, create a [`DataViewRowCursor`](xref:Microsoft.ML.DataViewRowCursor) by using the [`GetRowCursor`](xref:Microsoft.ML.IDataView.GetRowCursor*) method and passing in the [DataViewSchema](xref:Microsoft.ML.DataViewSchema) of your [`IDataView`](xref:Microsoft.ML.IDataView) as a parameter. Then, to iterate over rows, use the [`MoveNext`](xref:Microsoft.ML.DataViewRowCursor.MoveNext*) cursor method along with [`ValueGetter`](xref:Microsoft.ML.ValueGetter%601) delegates to extract the respective values from each of the columns.
+[`IDataView`](xref:Microsoft.ML.IDataView) is lazily evaluated. To iterate over the rows of an [`IDataView`](xref:Microsoft.ML.IDataView) without converting to an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) as demonstrated in previous sections of this document, create a [`DataViewRowCursor`](xref:Microsoft.ML.DataViewRowCursor) by using the [`GetRowCursor`](xref:Microsoft.ML.IDataView.GetRowCursor%2A) method and passing in the [DataViewSchema](xref:Microsoft.ML.DataViewSchema) of your [`IDataView`](xref:Microsoft.ML.IDataView) as a parameter. Then, to iterate over rows, use the [`MoveNext`](xref:Microsoft.ML.DataViewRowCursor.MoveNext%2A) cursor method along with [`ValueGetter`](xref:Microsoft.ML.ValueGetter%601) delegates to extract the respective values from each of the columns.
 
 > [!IMPORTANT]
 > For performance purposes, vectors in ML.NET use [`VBuffer`](xref:Microsoft.ML.Data.VBuffer%601) instead of native collection types (that is, `Vector`,`float[]`).
@@ -146,7 +146,7 @@ using (DataViewRowCursor cursor = data.GetRowCursor(columns))
 > [!WARNING]
 > Do not use `Preview` in production code because it is intended for debugging and may reduce performance.
 
-The model building process is experimental and iterative. To preview what data would look like after pre-processing or training a machine learning model on a subset of the data, use the [`Preview`](xref:Microsoft.ML.DebuggerExtensions.Preview*) method which returns a [`DataDebuggerPreview`](xref:Microsoft.ML.Data.DataDebuggerPreview). The result is an object with `ColumnView` and `RowView` properties which are both an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) and contain the values in a particular column or row. Specify the number of rows to apply the transformation to with the `maxRows` parameter.
+The model building process is experimental and iterative. To preview what data would look like after pre-processing or training a machine learning model on a subset of the data, use the [`Preview`](xref:Microsoft.ML.DebuggerExtensions.Preview%2A) method which returns a [`DataDebuggerPreview`](xref:Microsoft.ML.Data.DataDebuggerPreview). The result is an object with `ColumnView` and `RowView` properties which are both an [`IEnumerable`](xref:System.Collections.Generic.IEnumerable%601) and contain the values in a particular column or row. Specify the number of rows to apply the transformation to with the `maxRows` parameter.
 
 ![Data Debugger Preview Object](./media/inspect-intermediate-data-ml-net/data-debugger-preview-01.png)
 

docs/machine-learning/how-to-guides/load-data-ml-net.md

@@ -62,7 +62,7 @@ ML.NET Operates through column names. If you want to change the name of a column
 
 ## Load data from a single file
 
-To load data from a file use the [`LoadFromTextFile`](xref:Microsoft.ML.TextLoaderSaverCatalog.LoadFromTextFile*) method along with the data model for the data to be loaded. Since `separatorChar` parameter is tab-delimited by default, change it for your data file as needed. If your file has a header, set the `hasHeader` parameter to `true` to ignore the first line in the file and begin to load data from the second line.
+To load data from a file use the [`LoadFromTextFile`](xref:Microsoft.ML.TextLoaderSaverCatalog.LoadFromTextFile%2A) method along with the data model for the data to be loaded. Since `separatorChar` parameter is tab-delimited by default, change it for your data file as needed. If your file has a header, set the `hasHeader` parameter to `true` to ignore the first line in the file and begin to load data from the second line.
 
 ```csharp
 //Create MLContext
@@ -78,7 +78,7 @@ In the event that your data is stored in multiple files, as long as the data sch
 
 ### Load from files in a single directory
 
-When all of your data files are in the same directory, use wildcards in the [`LoadFromTextFile`](xref:Microsoft.ML.TextLoaderSaverCatalog.LoadFromTextFile*) method.
+When all of your data files are in the same directory, use wildcards in the [`LoadFromTextFile`](xref:Microsoft.ML.TextLoaderSaverCatalog.LoadFromTextFile%2A) method.
 
 ```csharp
 //Create MLContext
@@ -90,7 +90,7 @@ IDataView data = mlContext.Data.LoadFromTextFile<HousingData>("Data/*", separato
 
 ### Load from files in multiple directories
 
-To load data from multiple directories, use the [`CreateTextLoader`](xref:Microsoft.ML.TextLoaderSaverCatalog.CreateTextLoader*) method to create a [`TextLoader`](xref:Microsoft.ML.Data.TextLoader). Then, use the [`TextLoader.Load`](xref:Microsoft.ML.DataLoaderExtensions.Load*) method and specify the individual file paths (wildcards can't be used).
+To load data from multiple directories, use the [`CreateTextLoader`](xref:Microsoft.ML.TextLoaderSaverCatalog.CreateTextLoader%2A) method to create a [`TextLoader`](xref:Microsoft.ML.Data.TextLoader). Then, use the [`TextLoader.Load`](xref:Microsoft.ML.DataLoaderExtensions.Load%2A) method and specify the individual file paths (wildcards can't be used).
 
 ```csharp
 //Create MLContext
@@ -196,10 +196,10 @@ HousingData[] inMemoryCollection = new HousingData[]
 };
 ```
 
-Load the in-memory collection into an [`IDataView`](xref:Microsoft.ML.IDataView) with the [`LoadFromEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.LoadFromEnumerable*) method:
+Load the in-memory collection into an [`IDataView`](xref:Microsoft.ML.IDataView) with the [`LoadFromEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.LoadFromEnumerable%2A) method:
 
 > [!IMPORTANT]
-> [`LoadFromEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.LoadFromEnumerable*) assumes that the [`IEnumerable`](xref:System.Collections.IEnumerable) it loads from is thread-safe.
+> [`LoadFromEnumerable`](xref:Microsoft.ML.DataOperationsCatalog.LoadFromEnumerable%2A) assumes that the [`IEnumerable`](xref:System.Collections.IEnumerable) it loads from is thread-safe.
 
 ```csharp
 // Create MLContext

docs/machine-learning/how-to-guides/machine-learning-model-predictions-ml-net.md

@@ -68,7 +68,7 @@ To make a single prediction, create a [`PredictionEngine`](xref:Microsoft.ML.Pre
 PredictionEngine<HousingData, HousingPrediction> predictionEngine = mlContext.Model.CreatePredictionEngine<HousingData, HousingPrediction>(predictionPipeline);
 ```
 
-Then, use the [`Predict`](xref:Microsoft.ML.PredictionEngineBase%602.Predict*) method and pass in your input data as a parameter. Notice that using the [`Predict`](xref:Microsoft.ML.PredictionEngineBase%602.Predict*) method does not require the input to be an [`IDataView`](xref:Microsoft.ML.IDataView)). This is because it conveniently internalizes the input data type manipulation so you can pass in an object of the input data type. Additionally, since `CurrentPrice` is the target or label you're trying to predict using new data, it's assumed there is no value for it at the moment.
+Then, use the [`Predict`](xref:Microsoft.ML.PredictionEngineBase%602.Predict%2A) method and pass in your input data as a parameter. Notice that using the [`Predict`](xref:Microsoft.ML.PredictionEngineBase%602.Predict%2A) method does not require the input to be an [`IDataView`](xref:Microsoft.ML.IDataView)). This is because it conveniently internalizes the input data type manipulation so you can pass in an object of the input data type. Additionally, since `CurrentPrice` is the target or label you're trying to predict using new data, it's assumed there is no value for it at the moment.
 
 ```csharp
 // Input Data
@@ -110,14 +110,14 @@ HousingData[] housingData = new HousingData[]
 };
 ```
 
-Then, use the [`Transform`](xref:Microsoft.ML.ITransformer.Transform*) method to apply the data transformations and generate predictions.
+Then, use the [`Transform`](xref:Microsoft.ML.ITransformer.Transform%2A) method to apply the data transformations and generate predictions.
 
 ```csharp
 // Predicted Data
 IDataView predictions = predictionPipeline.Transform(inputData);
 ```
 
-Inspect the predicted values by using the [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn*) method.
+Inspect the predicted values by using the [`GetColumn`](xref:Microsoft.ML.Data.ColumnCursorExtensions.GetColumn%2A) method.
 
 ```csharp
 // Get Predictions