renamed master to main (dotnet#5719)

Author: michaelgsharp

README.md

@@ -22,7 +22,7 @@ See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/ap
 
 We have a GitHub repo with [ML.NET sample apps](https://github.com/dotnet/machinelearning-samples) with many scenarios such as Sentiment analysis, Fraud detection, Product Recommender, Price Prediction, Anomaly Detection, Image Classification, Object Detection and many more.
 
-In addition to the ML.NET samples provided by Microsoft, we're also highlighting many more samples created by the community showcased in this separate page [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/master/docs/COMMUNITY-SAMPLES.md)
+In addition to the ML.NET samples provided by Microsoft, we're also highlighting many more samples created by the community showcased in this separate page [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md)
 
 
 ## ML.NET videos playlist at YouTube

build/.night-build.yml

@@ -14,7 +14,6 @@ schedules:
   displayName: Nightly Build at midnight
   branches:
     include:
-    - master
     - main
     - releases/1.5.5
     - features/automl

build/.outer-loop-build.yml

@@ -14,7 +14,6 @@ schedules:
   displayName: outer loop build at midnight
   branches:
     include:
-    - master
     - main
     - releases/1.5.5
     - features/automl

docs/README.md

@@ -27,7 +27,7 @@ Building from Source
 Repo of Samples
 ====================
 
-- [ML.NET Samples](https://github.com/dotnet/machinelearning-samples/blob/master/README.md)
+- [ML.NET Samples](https://github.com/dotnet/machinelearning-samples/blob/main/README.md)
 
 Extensions for ML.NET
 ====================

docs/release-notes/1.3.1/release-1.3.1.md

@@ -73,7 +73,7 @@
 - **Stop LightGbm Warning for Default Metric Input:** Fixes warning, LightGBM `Warning Unknown parameter metric=` is produced when the default metric is used. ([#3965](https://github.com/dotnet/machinelearning/pull/40079))
 
 ## **Samples**
-- [**Fraud Detection using the anomaly detection PCA trainer**](https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/getting-started/AnomalyDetection_CreditCardFraudDetection)
+- [**Fraud Detection using the anomaly detection PCA trainer**](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/AnomalyDetection_CreditCardFraudDetection)
 
 ## **Breaking Changes**
 None

docs/release-notes/1.4.0/release-1.4.0.md

@@ -65,14 +65,14 @@
 
   [Design specification](https://github.com/dotnet/machinelearning/pull/3857)
 
-  [Sample](https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/getting-started/DatabaseLoader)
+  [Sample](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DatabaseLoader)
 
   [How to doc](https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/load-data-ml-net#load-data-from-a-relational-database)
 
 - **General Availability of PredictionEnginePool for scalable deployment**
   When deploying an ML model into multi-threaded and scalable .NET Core web applications and services (such as ASP .NET Core web apps, WebAPIs or an Azure Function) it is recommended to use the PredictionEnginePool instead of directly creating the PredictionEngine object on every request due to performance and scalability reasons. For further background information on why the PredictionEnginePool is recommended, read [this](https://devblogs.microsoft.com/cesardelatorre/how-to-optimize-and-run-ml-net-models-on-scalable-asp-net-core-webapis-or-web-apps/) blog post.
 
-  [Sample](https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg)
+  [Sample](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg)
 
 - **General Availability of Enhanced for .NET Core 3.0**
   This means ML .NET can take advantage of the new features when running in a .NET Core 3.0 application. The first new feature we are using is the new hardware intrinsics feature, which allows .NET code to accelerate math operations by using processor specific instructions.

docs/specs/mlnet-cli/MLNET-CLI-Specs.md

@@ -782,7 +782,7 @@ As introduced, the CLI will generate the following assets as result of the opera
 
 The training code for a single generated console app project should be similar to the following sample app:
 
-- Sentiment Analysis sample: https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/SentimentAnalysis/SentimentAnalysisConsoleApp/Program.cs
+- Sentiment Analysis sample: https://github.com/dotnet/machinelearning-samples/blob/main/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/SentimentAnalysis/SentimentAnalysisConsoleApp/Program.cs
 
    Notes:
     - This sample code is evolving when upgrading to upcoming preview versions, such as 0.10, 0.11, etc.)