Edits for Acrolinx

davidpeterh · davidpeterh · commit c41da850871b · 2020-07-14T20:40:37.000-04:00
diff --git a/docs/machine-learning/python/ref-py-revoscalepy.md b/docs/machine-learning/python/ref-py-revoscalepy.md
@@ -62,7 +62,7 @@ SQL Server and Python use different data types in some cases. For a list of mapp
 
 | Function | Description |
 |----------|-------------|
-|[rx_import](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-import) | Import data into an .xdf file or data frame.|
+|[rx_import](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-import) | Import data into a .xdf file or data frame.|
 |[rx_data_step](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-data-step) | Transform data from an input data set to an output data set.|
 
 <a name="bkmk_algorithms"></a>
@@ -103,7 +103,7 @@ Inputs and outputs must be **pandas** data frames. When this is done, you can ca
 
 ### Using revoscalepy with microsoftml
 
-The Python functions for [microsoftml](ref-py-microsoftml.md) are integrated with the compute contexts and data sources that are provided in revoscalepy. When calling functions from microsoftml, for example when defining and training a model, use the revoscalepy functions to execute the Python code either locally or in a SQl Server remote compute context.
+The Python functions for [microsoftml](ref-py-microsoftml.md) are integrated with the compute contexts and data sources that are provided in revoscalepy. When calling functions from microsoftml, for example when defining and training a model, use the revoscalepy functions to execute the Python code either locally or in a SQL Server remote compute context.
 
 The following example shows the syntax for importing modules in your Python code. You  can then reference the individual functions you need.
 
diff --git a/docs/machine-learning/r/ref-r-microsoftml.md b/docs/machine-learning/r/ref-r-microsoftml.md
@@ -66,7 +66,7 @@ This section lists the functions by category to give you an idea of how each one
 |[concat](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/concat) | Transformation to create a single vector-valued column from multiple columns.  |
 |[categorical](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/categorical) | Create indicator vector using categorical transform with dictionary.  |
 |[categoricalHash](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/categoricalhash) | Converts the categorical value into an indicator array by hashing. |
-|[featurizeText](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/featurizetext) | Produces a bag of counts of sequences of consecutive words, called n-grams, from a given corpus of text. It offers language detection, tokenization, stopwords removing, text normalization and feature generation.  |
+|[featurizeText](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/featurizetext) | Produces a bag of counts of sequences of consecutive words, called n-grams, from a given corpus of text. It offers language detection, tokenization, stopwords removing, text normalization, and feature generation.  |
 |[getSentiment](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/getsentiment) | Scores natural language text and creates a column that contains probabilities that the sentiments in the text are positive.|
 |[ngram](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/ngram) | allows defining arguments for count-based and hash-based feature extraction.|
 |[selectColumns](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/selectcolumns) | Selects a set of columns to retrain, dropping all others. |
diff --git a/docs/machine-learning/r/ref-r-revoscaler.md b/docs/machine-learning/r/ref-r-revoscaler.md
@@ -1,6 +1,6 @@
 ---
 title: RevoScaleR R package
-description: RevoScaleR is an R package from Microsoft that supports distributed computing, remote compute contexts, and high-performance data science algorithms. It also supports data import, data transformation, summarization, visualization, and analysis.The package is included in SQL Server Machine Learning Services and SQL Server 2016 R Services.
+description: RevoScaleR is an R package from Microsoft that supports distributed computing, remote compute contexts, and high-performance data science algorithms. It also supports data import, data transformation, summarization, visualization, and analysis. The package is included in SQL Server Machine Learning Services and SQL Server 2016 R Services.
 ms.prod: sql
 ms.technology: machine-learning-services
 ms.date: 07/14/2020
@@ -15,9 +15,9 @@ monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allv
 
 **RevoScaleR** is an R package from Microsoft that supports distributed computing, remote compute contexts, and high-performance data science algorithms. It also supports data import, data transformation, summarization, visualization, and analysis. The package is included in [SQL Server Machine Learning Services](../sql-server-machine-learning-services.md) and [SQL Server 2016 R Services](sql-server-r-services.md).
 
-In contrast with base R functions, RevoScaleR operations can be performed against very large datasets, in parallel, and on distributed file systems. Functions can operate over datasets that do not fit in memory by using chunking and by reassembling results when operations are complete.
+In contrast with base R functions, RevoScaleR operations can be performed against large datasets, in parallel, and on distributed file systems. Functions can operate over datasets that do not fit in memory by using chunking and by reassembling results when operations are complete.
 
-RevoScaleR functions are denoted with an **rx** or **Rx** prefix to make them easy to identify.
+RevoScaleR functions are denoted with a rx** or **Rx** prefix to make them easy to identify.
 
 RevoScaleR serves as a platform for distributed data science. For example, you can use the RevoScaleR compute contexts and transformations with the state-of-the-art algorithms in [MicrosoftML](https://docs.microsoft.com/machine-learning-server/r/concept-what-is-the-microsoftml-package). You can also use [rxExec](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxexec) to run base R functions in parallel.
 
@@ -85,7 +85,7 @@ After you have created a data source object, you can use the object to load data
 |---------------|-------------|
 |[rxHistogram](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxhistogram)  |Creates a histogram from data. | 
 |[rxLinePlot](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxlineplot) |Creates a line plot from data. | 
-|[rxLorenz](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxlorenz)  |Computes a Lorenz curve which can be plotted. | 
+|[rxLorenz](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxlorenz)  |Computes a Lorenz curve, which can be plotted. | 
 |[rxRocCurve](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxroc)  |Computes and plots ROC curves from actual and predicted data. | 
 
 <a name="statistics-functions"></a>
diff --git a/docs/machine-learning/r/ref-r-sqlrutils.md b/docs/machine-learning/r/ref-r-sqlrutils.md
@@ -19,7 +19,7 @@ By converting your R code to run within a single stored procedure, you can make
 The **sqlrutils** package performs these tasks:
 
 - Saves the generated T-SQL script as a string inside an R data structure
-- Optionally, generates a .sql file for the T-SQL script, which you can edit or run to create a stored procedure
+- Optionally, generate a .sql file for the T-SQL script, which you can edit or run to create a stored procedure
 - Registers the newly created stored procedure with the SQL Server instance from your R development environment
 
 You can also execute the stored procedure from an R environment, by passing well-formed parameters and processing the results. Or, you can use the stored procedure from SQL Server to support common database integration scenarios such as ETL, model training, and high-volume scoring.
