dotnet · artidoro · Feb 27, 2019 · Feb 26, 2019 · Feb 26, 2019 · Feb 26, 2019
diff --git a/docs/code/MlNetCookBook.md b/docs/code/MlNetCookBook.md
diff --git a/docs/code/experimental/MlNetCookBookStaticApi.md b/docs/code/experimental/MlNetCookBookStaticApi.md
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Calibrator.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Calibrator.cs
@@ -28,7 +28,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Create a text loader.
-            var reader = mlContext.Data.CreateTextLoader(new TextLoader.Options()
+            var loader = mlContext.Data.CreateTextLoader(new TextLoader.Options()
             {
                 Separators = new[] { '\t' },
                 HasHeader = true,
@@ -39,8 +39,8 @@ public static void Example()
                     }
             });
 
-            // Read the data
-            var data = reader.Read(dataFile);
+            // Load the data
+            var data = loader.Load(dataFile);
 
             // Split the dataset into two parts: one used for training, the other to train the calibrator
             var split = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs
@@ -14,7 +14,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
             IEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorSample> enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorSamples(5);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Look at the original dataset
             Console.WriteLine($"Label\tFeatures[0]");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs
@@ -20,8 +20,8 @@ public static void Example()
             // Get a small dataset as an IEnumerable.
             IEnumerable<DatasetUtils.SampleTemperatureData> enumerableOfData = DatasetUtils.GetSampleTemperatureData(5);
 
-            // Read in into an IDataView using ReadFromEnumerable. 
-            IDataView data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            // Load dataset into an IDataView. 
+            IDataView data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // We can now examine the records in the IDataView. We first create an enumerable of rows in the IDataView.
             var rowEnumerable = mlContext.Data.CreateEnumerable<DatasetUtils.SampleTemperatureData>(data, reuseRowObject: true);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             IEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs
@@ -20,7 +20,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             IEnumerable<MulticlassClassificationExample> enumerableOfData = DatasetUtils.GenerateRandomMulticlassClassificationExamples(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Convert the string labels to keys
             var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var dataEnumerable = DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(10, naRate: 0.05);
-            var data = mlContext.Data.ReadFromEnumerable(dataEnumerable);
+            var data = mlContext.Data.LoadFromEnumerable(dataEnumerable);
 
             // Look at the original dataset
             Console.WriteLine($"Label\tFeatures");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var enumerableOfData = DatasetUtils.GetSampleTemperatureData(5);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/FastTreeRegression.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/FastTreeRegression.cs
@@ -14,7 +14,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var data = SamplesUtils.DatasetUtils.GetInfertData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureSelectionTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureSelectionTransform.cs
@@ -28,9 +28,9 @@ public static void Example()
             // as well as the source of randomness.
             var ml = new MLContext();
 
-            // First, we define the reader: specify the data columns and where to find them in the text file. Notice that we combine entries from
+            // First, we define the loader: specify the data columns and where to find them in the text file. Notice that we combine entries from
             // all the feature columns into entries of a vector of a single column named "Features".
-            var reader = ml.Data.CreateTextLoader(
+            var loader = ml.Data.CreateTextLoader(
                 columns: new[]
                     {
                         new TextLoader.Column("Label", DataKind.Boolean, 0),
@@ -39,8 +39,8 @@ public static void Example()
                 hasHeader: true
             );
 
-            // Then, we use the reader to read the data as an IDataView.
-            var data = reader.Read(dataFilePath);
+            // Then, we use the loader to load the data as an IDataView.
+            var data = loader.Load(dataFilePath);
 
             // Second, we define the transformations that we apply on the data. Remember that an Estimator does not transform data
             // directly, but it needs to be trained on data using .Fit(), and it will output a Transformer, which can transform data.

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/IidChangePointDetectorTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/IidChangePointDetectorTransform.cs
@@ -46,7 +46,7 @@ public static void IidChangePointDetectorTransform()
                 data.Add(new IidChangePointData(7));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(ChangePointPrediction.Prediction);
@@ -104,7 +104,7 @@ public static void IidChangePointDetectorPrediction()
                 data.Add(new IidChangePointData(7));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(ChangePointPrediction.Prediction);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/IidSpikeDetectorTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/IidSpikeDetectorTransform.cs
@@ -43,7 +43,7 @@ public static void IidSpikeDetectorTransform()
                 data.Add(new IidSpikeData(5));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(IidSpikePrediction.Prediction);
@@ -93,7 +93,7 @@ public static void IidSpikeDetectorPrediction()
                 data.Add(new IidSpikeData(5));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(IidSpikePrediction.Prediction);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ConvertToGrayScale.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ConvertToGrayScale.cs
@@ -30,7 +30,7 @@ public static void Example()
                         new TextLoader.Column("ImagePath", DataKind.String, 0),
                         new TextLoader.Column("Name", DataKind.String, 1),
                  }
-            }).Read(imagesDataFile);
+            }).Load(imagesDataFile);
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ExtractPixels.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ExtractPixels.cs
@@ -31,7 +31,7 @@ public static void Example()
                         new TextLoader.Column("ImagePath", DataKind.String, 0),
                         new TextLoader.Column("Name", DataKind.String, 1),
                 }
-            }).Read(imagesDataFile);
+            }).Load(imagesDataFile);
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/LoadImages.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/LoadImages.cs
@@ -30,7 +30,7 @@ public static void Example()
                         new TextLoader.Column("ImagePath", DataKind.String, 0),
                         new TextLoader.Column("Name", DataKind.String, 1),
                     }
-            }).Read(imagesDataFile);
+            }).Load(imagesDataFile);
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ResizeImages.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ResizeImages.cs
@@ -30,7 +30,7 @@ public static void Example()
                         new TextLoader.Column("ImagePath", DataKind.String, 0),
                         new TextLoader.Column("Name", DataKind.String, 1),
                 }
-            }).Read(imagesDataFile);
+            }).Load(imagesDataFile);
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/KMeans.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/KMeans.cs
@@ -13,7 +13,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var data = SamplesUtils.DatasetUtils.GetInfertData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/KeyToValueValueToKey.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/KeyToValueValueToKey.cs
@@ -15,7 +15,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and load it into ML.NET data set.
             IEnumerable<SamplesUtils.DatasetUtils.SampleTopicsData> data = SamplesUtils.DatasetUtils.GetTopicsData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of one of the columns of the the topics data. 
             // The Review column contains the keys associated with a particular body of text.  

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
@@ -14,7 +14,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and then read it as a ML.NET data set.
             IEnumerable<SamplesUtils.DatasetUtils.SampleTopicsData> data = SamplesUtils.DatasetUtils.GetTopicsData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of one of the columns of the the topics data. 
             // The Review column contains the keys associated with a particular body of text.  

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs
@@ -31,7 +31,7 @@ public static void Example()
             // 14. Column: native-country (text/categorical)
             // 15. Column: Column [Label]: IsOver50K (boolean)
 
-            var reader = ml.Data.CreateTextLoader(new TextLoader.Options
+            var loader = ml.Data.CreateTextLoader(new TextLoader.Options
             {
                 Separators = new[] { ',' },
                 HasHeader = true,
@@ -55,7 +55,7 @@ public static void Example()
                 }
             });
 
-            IDataView data = reader.Read(dataFilePath);
+            IDataView data = loader.Load(dataFilePath);
 
             var split = ml.BinaryClassification.TrainTestSplit(data, testFraction: 0.2);
 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs
@@ -14,7 +14,7 @@ public static void NgramTransform()
 
             // Get a small dataset as an IEnumerable and convert to IDataView.
             IEnumerable<SamplesUtils.DatasetUtils.SampleSentimentData> data = SamplesUtils.DatasetUtils.GetSentimentData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs
@@ -16,7 +16,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             IEnumerable<SamplesUtils.DatasetUtils.SampleInfertData> data = SamplesUtils.DatasetUtils.GetInfertData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
@@ -34,7 +34,7 @@ public static void Example()
             // Create ML pipeline to score the data using OnnxScoringEstimator
             var mlContext = new MLContext();
             var data = GetTensorData();
-            var idv = mlContext.Data.ReadFromEnumerable(data);
+            var idv = mlContext.Data.LoadFromEnumerable(data);
             var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath, new[] { outputInfo.Key }, new[] { inputInfo.Key });
 
             // Run the pipeline and get the transformed values

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs
@@ -15,7 +15,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             IEnumerable<SamplesUtils.DatasetUtils.SampleVectorOfNumbersData> data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/SDCARegression.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/SDCARegression.cs
@@ -14,7 +14,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var data = SamplesUtils.DatasetUtils.GetInfertData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/SsaChangePointDetectorTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/SsaChangePointDetectorTransform.cs
@@ -41,7 +41,7 @@ public static void SsaChangePointDetectorTransform()
                 data.Add(new SsaChangePointData(i * 100));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup SsaChangePointDetector arguments
             var inputColumnName = nameof(SsaChangePointData.Value);
@@ -102,7 +102,7 @@ public static void SsaChangePointDetectorPrediction()
                     data.Add(new SsaChangePointData(j));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup SsaChangePointDetector arguments
             var inputColumnName = nameof(SsaChangePointData.Value);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/SsaSpikeDetectorTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/SsaSpikeDetectorTransform.cs
@@ -48,7 +48,7 @@ public static void SsaSpikeDetectorTransform()
                 data.Add(new SsaSpikeData(i));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             var inputColumnName = nameof(SsaSpikeData.Value);
@@ -110,7 +110,7 @@ public static void SsaSpikeDetectorPrediction()
                     data.Add(new SsaSpikeData(j));
 
             // Convert data to IDataView.
-            var dataView = ml.Data.ReadFromEnumerable(data);
+            var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             var inputColumnName = nameof(SsaSpikeData.Value);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/StopWordRemoverTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/StopWordRemoverTransform.cs
@@ -15,7 +15,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert to IDataView.
             var data = SamplesUtils.DatasetUtils.GetSentimentData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs
@@ -17,7 +17,7 @@ public static void Example()
 
             var mlContext = new MLContext();
             var data = GetTensorData();
-            var idv = mlContext.Data.ReadFromEnumerable(data);
+            var idv = mlContext.Data.LoadFromEnumerable(data);
 
             // Create a ML pipeline.
             var pipeline = mlContext.Transforms.ScoreTensorFlowModel(

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs
@@ -30,10 +30,10 @@ public static void Example()
                 "such a big profile for the whole film but these children are amazing and should be praised " +
                 "for what they have done don't you think the whole story was so lovely because it was true " +
                 "and was someone's life after all that was shared with us all" } };
-            var dataView = mlContext.Data.ReadFromEnumerable(data);
+            var dataView = mlContext.Data.LoadFromEnumerable(data);
 
             // This is the dictionary to convert words into the integer indexes.
-            var lookupMap = mlContext.Data.ReadFromTextFile(Path.Combine(modelLocation, "imdb_word_index.csv"),
+            var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(modelLocation, "imdb_word_index.csv"),
                 columns: new[]
                    {
                         new TextLoader.Column("Words", DataKind.String, 0),

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs
@@ -15,7 +15,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and convert to IDataView.
             var data = SamplesUtils.DatasetUtils.GetSentimentData();
-            var trainData = ml.Data.ReadFromEnumerable(data);
+            var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
             //

diff --git a/...ples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs b/...ples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs
@@ -23,9 +23,9 @@ public static void Example()
             // as a catalog of available operations and as the source of randomness.
             var mlContext = new MLContext();
 
-            // Step 1: Read the data as an IDataView.
-            // First, we define the reader: specify the data columns and where to find them in the text file.
-            var reader = mlContext.Data.CreateTextLoader(
+            // Step 1: Load the data as an IDataView.
+            // First, we define the loader: specify the data columns and where to find them in the text file.
+            var loader = mlContext.Data.CreateTextLoader(
                 columns: new[]
                     {
                         new TextLoader.Column("Sentiment", DataKind.Boolean, 0),
@@ -34,8 +34,8 @@ public static void Example()
                 hasHeader: true
             );
 
-            // Read the data
-            var data = reader.Read(dataFile);
+            // Load the data
+            var data = loader.Load(dataFile);
 
             // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times, it can be slow due to
             // expensive featurization and disk operations. When the considered data can fit into memory, a solution is to cache the data in memory. Caching is especially

diff --git a/...es/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCASupportVectorMachine.cs b/...es/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCASupportVectorMachine.cs
@@ -32,7 +32,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Step 1: Read the data as an IDataView.
-            var data = mlContext.Data.ReadFromEnumerable(rawData);
+            var data = mlContext.Data.LoadFromEnumerable(rawData);
 
             // ML.NET doesn't cache data set by default. Caching is always recommended when using the
             // StochasticDualCoordinateAscent algorithm because it may incur multiple data passes.