Merge pull request #57558 from garyericson/11-08-tdsp-selectors-2

Removing selectors from TDSP articles - 2

Author: Ja-Dunn

articles/machine-learning/team-data-science-process/create-features-hive.md

@@ -25,9 +25,7 @@ The operations needed to create features can be memory intensive. The performanc
 
 Examples of the queries that are presented are specific to the [NYC Taxi Trip Data](http://chriswhong.com/open-data/foil_nyc_taxi/) scenarios are also provided in [GitHub repository](https://github.com/Azure/Azure-MachineLearning-DataScience/tree/master/Misc/DataScienceProcess/DataScienceScripts). These queries already have data schema specified and are ready to be submitted to run. In the final section, parameters that users can tune so that the performance of Hive queries can be improved are  also discussed.
 
-[!INCLUDE [cap-create-features-data-selector](../../../includes/cap-create-features-selector.md)]
-
-This **menu** links to topics that describe how to create features for data in various environments. This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
+This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
 
 ## Prerequisites
 This article assumes that you have:

articles/machine-learning/team-data-science-process/create-features-sql-server.md

@@ -21,9 +21,7 @@ ms.author: deguhath
 # Create features for data in SQL Server using SQL and Python
 This document shows how to generate features for data stored in a SQL Server VM on Azure that help algorithms learn more efficiently from the data. You can use SQL or a programming language like Python to accomplish this task. Both approaches are demonstrated here.
 
-[!INCLUDE [cap-create-features-data-selector](../../../includes/cap-create-features-selector.md)]
-
-This **menu** links to topics that describe how to create features for data in various environments. This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
+This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
 
 > [!NOTE]
 > For a practical example, you can consult the [NYC Taxi dataset](http://www.andresmh.com/nyctaxitrips/) and refer to the IPNB titled [NYC Data wrangling using IPython Notebook and SQL Server](https://github.com/Azure/Azure-MachineLearning-DataScience/blob/master/Misc/DataScienceProcess/iPythonNotebooks/machine-Learning-data-science-process-sql-walkthrough.ipynb) for an end-to-end walk-through.

articles/machine-learning/team-data-science-process/create-features.md

@@ -21,9 +21,7 @@ ms.author: deguhath
 # Feature engineering in data science
 This article explains the purposes of feature engineering and provides examples of its role in the data enhancement process of machine learning. The examples used to illustrate this process are drawn from Azure Machine Learning Studio. 
 
-[!INCLUDE [cap-create-features-data-selector](../../../includes/cap-create-features-selector.md)]
-
-This **menu** links to articles that describe how to create features for data in various environments. This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
+This task is a step in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/).
 
 Feature engineering attempts to increase the predictive power of learning algorithms by creating features from raw data that help facilitate the learning process. The engineering and selection of features is one part of the TDSP outlined in the [What is the Team Data Science Process lifecycle?](overview.md) Feature engineering and selection are parts of the **Develop features** step of the TDSP. 
 
@@ -34,6 +32,11 @@ Normally **feature engineering** is applied first to generate additional feature
 
 The training data used in machine learning can often be enhanced by extraction of features from the raw data collected. An example of an engineered feature in the context of learning how to classify the images of handwritten characters is creation of a bit density map constructed from the raw bit distribution data. This map can help locate the edges of the characters more efficiently than simply using the raw distribution directly.
 
+To create features for data in specific environments, see the following articles:
+
+* [Create features for data in SQL Server](create-features-sql-server.md)
+* [Create features for data in a Hadoop cluster using Hive queries](create-features-hive.md)
+
 [!INCLUDE [machine-learning-free-trial](../../../includes/machine-learning-free-trial.md)]
 
 ## Create features from your data - feature engineering