diff --git a/docs/machine-learning/tutorials/sentiment-analysis.md b/docs/machine-learning/tutorials/sentiment-analysis.md
index 0af5c53eaa787..1d6af91aa9609 100644
--- a/docs/machine-learning/tutorials/sentiment-analysis.md
+++ b/docs/machine-learning/tutorials/sentiment-analysis.md
@@ -101,8 +101,8 @@ You need to create two global variables to hold the path to the recently downloa
 Add the following code to the line right above the `Main` method:
 
 ```csharp
-const string _dataPath = @"..\..\data\sentiment labelled sentences\imdb_labelled.txt";
-const string _testDataPath = @"..\..\data\sentiment labelled sentences\yelp_labelled.txt";
+const string _dataPath = @"..\..\..\data\imdb_labelled.txt";
+const string _testDataPath = @"..\..\..\data\yelp_labelled.txt";
 ```
 
 You need to create some classes for your input data and predictions. Add a new class to your project:
@@ -122,8 +122,9 @@ Add the following code, which has two classes `SentimentData` and `SentimentPred
 ```csharp
 public class SentimentData
 {
+    [Column(ordinal: "0")]
     public string SentimentText;
-    [ColumnName("Label")]
+    [Column(ordinal: "1", name: "Label")]
     public float Sentiment;
 }
 
@@ -134,7 +135,7 @@ public class SentimentPrediction
 }
 ```
 
-`SentimentData` is the input dataset class and has a string for the comment (`SentimentText`), a boolean (`Sentiment`) that has a value for sentiment of either positive or negative, and a `Label` `ColumnName` attribute. `SentimentPrediction` is the class used for prediction after the model has been trained. It has a single boolean (`Sentiment`) and a `PredictedLabel` `ColumnName` attribute. The `Label` is used to create and train the model, and it's also used with a second dataset to evaluate the model. The `PredictedLabel` is used during prediction and evaluation. For evaluation, an input with training data, the predicted values, and the model are used.
+`SentimentData` is the input dataset class and has a string for the comment (`SentimentText`), a `float` (`Sentiment`) that has a value for sentiment of either positive or negative. Both fields have `Column` attributes attached to them. This attribute describes the order of each field in the data file, and which is the `Label` field. `SentimentPrediction` is the class used for prediction after the model has been trained. It has a single boolean (`Sentiment`) and a `PredictedLabel` `ColumnName` attribute. The `Label` is used to create and train the model, and it's also used with a second dataset to evaluate the model. The `PredictedLabel` is used during prediction and evaluation. For evaluation, an input with training data, the predicted values, and the model are used.
 
 In the *Program.cs* file, replace the `Console.WriteLine("Hello World!")` line with the following code in the `Main` method:
 
@@ -169,7 +170,7 @@ var pipeline = new LearningPipeline();
 The <xref:Microsoft.ML.TextLoader%601> object is the first part of the pipeline, and loads the training file data.
 
 ```csharp
-pipeline.Add(new TextLoader<SentimentData>(_dataPath, header: false, sep: "tab"));
+pipeline.Add(new TextLoader<SentimentData>(_dataPath, useHeader: false, separator: "tab"));
 ```
 
 ## Data preprocess and feature engineering
@@ -299,7 +300,7 @@ Evaluate(model);
 The <xref:Microsoft.ML.TextLoader%601> class loads the new test dataset with the same schema. You can evaluate the model using this dataset as a quality check. Add that next to the `Evaluate` method call, using the following code:
 
 ```csharp
-var testData = new TextLoader<SentimentData>(_testDataPath, header: false, sep: "tab");
+var testData = new TextLoader<SentimentData>(_testDataPath, useHeader: false, separator: "tab");
 ```
 
 The <xref:Microsoft.ML.Models.BinaryClassificationEvaluator> object computes the quality metrics for the `PredictionModel` using the specified dataset. To see those metrics, add the evaluator as the next line in the `Evaluate` method, with the following code:
diff --git a/docs/machine-learning/tutorials/taxi-fare.md b/docs/machine-learning/tutorials/taxi-fare.md
index 94512dbef3073..1692e7834d243 100644
--- a/docs/machine-learning/tutorials/taxi-fare.md
+++ b/docs/machine-learning/tutorials/taxi-fare.md
@@ -105,21 +105,25 @@ Next, create classes for the input data and the predictions:
 using Microsoft.ML.Runtime.Api;
 ```
 
-Add two classes into this file. `TaxiTrip`, the input data set class, has definitions for each of the columns discovered above and a `Label` `ColumnName` attribute. Add the following code to the file:
+Add two classes into this file. `TaxiTrip`, the input data set class, has definitions for each of the columns discovered above and a `Label` attribute for the fare_amount column that you are predicting. Add the following code to the file:
 
 ```csharp
 public class TaxiTrip
 {
+    [Column(ordinal: "0")]
     public string vendor_id;
+    [Column(ordinal: "1")]
     public string rate_code;
+    [Column(ordinal: "2")]
     public float passenger_count;
+    [Column(ordinal: "3")]
     public float trip_time_in_secs;
+    [Column(ordinal: "4")]
     public float trip_distance;
+    [Column(ordinal: "5")]
     public string payment_type;
+    [Column(ordinal: "6", "Label")]
     public float fare_amount;
-
-    [ColumnName("Label")]
-    public float Label;
 }
 ```