Remove Runtime in namespace from docs folder.

docs/code/EntryPoints.md

@@ -223,7 +223,7 @@ parameter.
 1. Add a `LoadableClass` assembly attribute with the `SignatureEntryPointModule` signature as shown [here](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L27).  
 2. Create a public static method, that:
     1. Takes an object representing the arguments of the component you want to expose as shown [here](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L414)
-    2. Initializes and runs the component, returning one of the nested classes of  [`Microsoft.ML.Runtime.EntryPoints.CommonOutputs`](https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Data/EntryPoints/CommonOutputs.cs)
+    2. Initializes and runs the component, returning one of the nested classes of  [`Microsoft.ML.EntryPoints.CommonOutputs`](https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Data/EntryPoints/CommonOutputs.cs)
     3. Is annotated with the [`TlcModule.EntryPoint`](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L407) attribute
 
 For an example of a transformer as an entrypoint, see [OneHotVectorizer](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.Transforms/OneHotEncoding.cs#L283). 

docs/code/SchemaComprehension.md

@@ -65,7 +65,7 @@ static void Main(string[] args)
     };
 
     // Create the ML.NET environment.
-    var env = new Microsoft.ML.Runtime.Data.TlcEnvironment();
+    var env = new Microsoft.ML.Data.TlcEnvironment();
 
     // Create the data view.
     // This method will use the definition of IrisData to understand what columns there are in the 
@@ -74,7 +74,7 @@ static void Main(string[] args)
 
     // Now let's do something to the data view. For example, concatenate all four non-label columns
     // into 'Features' column.
-    dv = new Microsoft.ML.Runtime.Data.ConcatTransform(env, dv, "Features", 
+    dv = new Microsoft.ML.Data.ConcatTransform(env, dv, "Features", 
         "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
 
     // Read the data into an another array, this time we read the 'Features' and 'Label' columns

docs/code/VBufferCareFeeding.md

@@ -224,17 +224,17 @@ ML.NET's runtime code has a number of utilities for operating over `VBuffer`s
 that we have written to be generally useful. We will not treat on these in
 detail here, but:
 
-* `Microsoft.ML.Runtime.Data.VBuffer<T>` itself contains a few methods for
+* `Microsoft.ML.Data.VBuffer<T>` itself contains a few methods for
   accessing and iterating over its values.
 
-* `Microsoft.ML.Runtime.Internal.Utilities.VBufferUtils` contains utilities
+* `Microsoft.ML.Internal.Utilities.VBufferUtils` contains utilities
   mainly for non-numeric manipulation of `VBuffer`s.
 
-* `Microsoft.ML.Runtime.Numeric.VectorUtils` contains math operations
+* `Microsoft.ML.Numeric.VectorUtils` contains math operations
   over `VBuffer<float>` and `float[]`, like computing norms, dot-products, and
   whatnot.
 
-* `Microsoft.ML.Runtime.Data.BufferBuilder<T>` is an abstract class whose
+* `Microsoft.ML.Data.BufferBuilder<T>` is an abstract class whose
   concrete implementations are used throughout ML.NET to build up `VBuffer<T>`
   instances. Note that if one *can* simply build a `VBuffer` oneself easily
   and do not need the niceties provided by the buffer builder, you should