freshness43

Author: dagiro

articles/hdinsight/spark/apache-spark-ipython-notebook-machine-learning.md

@@ -5,18 +5,18 @@ author: hrasheed-msft
 ms.author: hrasheed
 ms.reviewer: jasonh
 ms.service: hdinsight
-ms.custom: hdinsightactive,mvc
 ms.topic: tutorial
-ms.date: 06/26/2019
+ms.custom: hdinsightactive,mvc
+ms.date: 04/07/2020
 
 #customer intent: As a developer new to Apache Spark and to Apache Spark in Azure HDInsight, I want to learn how to create a simple machine learning Spark application.
 ---
 
 # Tutorial: Build an Apache Spark machine learning application in Azure HDInsight
 
-In this tutorial, you learn how to use the [Jupyter Notebook](https://jupyter.org/) to build an [Apache Spark](https://spark.apache.org/) machine learning application for Azure HDInsight.
+In this tutorial, you learn how to use the [Jupyter Notebook](https://jupyter.org/) to build an [Apache Spark](./apache-spark-overview.md) machine learning application for Azure HDInsight.
 
-[MLlib](https://spark.apache.org/docs/latest/ml-guide.html) is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
+[MLlib](https://spark.apache.org/docs/latest/ml-guide.html) is Spark's adaptable machine learning library consisting of common learning algorithms and utilities. (Classification, regression, clustering, collaborative filtering, and dimensionality reduction. Also, underlying optimization primitives.)
 
 In this tutorial, you learn how to:
 > [!div class="checklist"]
@@ -30,13 +30,13 @@ In this tutorial, you learn how to:
 
 ## Understand the data set
 
-The application uses the sample **HVAC.csv** data that is available on all clusters by default. The file is located at `\HdiSamples\HdiSamples\SensorSampleData\hvac`. The data shows the target temperature and the actual temperature of some buildings that have HVAC systems installed. The **System** column represents the system ID and the **SystemAge** column represents the number of years the HVAC system has been in place at the building. Using the data, you can predict whether a building will be hotter or colder based on the target temperature, given a system ID, and system age.
+The application uses the sample **HVAC.csv** data that is available on all clusters by default. The file is located at `\HdiSamples\HdiSamples\SensorSampleData\hvac`. The data shows the target temperature and the actual temperature of some buildings that have HVAC systems installed. The **System** column represents the system ID and the **SystemAge** column represents the number of years the HVAC system has been in place at the building. You can predict whether a building will be hotter or colder based on the target temperature, given system ID, and system age.
 
 ![Snapshot of data used for Spark machine learning example](./media/apache-spark-ipython-notebook-machine-learning/spark-machine-learning-understand-data.png "Snapshot of data used for Spark machine learning example")
 
 ## Develop a Spark machine learning application using Spark MLlib
 
-In this application, you use a Spark [ML pipeline](https://spark.apache.org/docs/2.2.0/ml-pipeline.html) to perform a document classification. ML Pipelines provide a uniform set of high-level APIs built on top of DataFrames that help users create and tune practical machine learning pipelines. In the pipeline, you split the document into words, convert the words into a numerical feature vector, and finally build a prediction model using the feature vectors and labels. Perform the following steps to create the application.
+This application uses a Spark [ML pipeline](https://spark.apache.org/docs/2.2.0/ml-pipeline.html) to do a document classification. ML Pipelines provide a uniform set of high-level APIs built on top of DataFrames. The DataFrames help users create and tune practical machine learning pipelines. In the pipeline, you split the document into words, convert the words into a numerical feature vector, and finally build a prediction model using the feature vectors and labels. Do the following steps to create the application.
 
 1. Create a Jupyter notebook using the PySpark kernel. For the instructions, see [Create a Jupyter notebook](./apache-spark-jupyter-spark-sql.md#create-a-jupyter-notebook).
 
@@ -140,9 +140,9 @@ In this application, you use a Spark [ML pipeline](https://spark.apache.org/docs
 
     ![Output data snapshot for Spark machine learning example](./media/apache-spark-ipython-notebook-machine-learning/spark-machine-learning-output-data.png "Output data snapshot for Spark machine learning example")
 
-    Notice how the actual temperature is less than the target temperature suggesting the building is cold. Hence in the training output, the value for **label** in the first row is **0.0**, which means the building is not hot.
+    Notice how the actual temperature is less than the target temperature suggesting the building is cold. The value for **label** in the first row is **0.0**, which means the building isn't hot.
 
-1. Prepare a data set to run the trained model against. To do so, you pass on a system ID and system age (denoted as **SystemInfo** in the training output), and the model predicts whether the building with that system ID and system age will be hotter (denoted by 1.0) or cooler (denoted by 0.0).
+1. Prepare a data set to run the trained model against. To do so, you pass on a system ID and system age (denoted as **SystemInfo** in the training output). The model predicts whether the building with that system ID and system age will be hotter (denoted by 1.0) or cooler (denoted by 0.0).
 
     ```PySpark
     # SystemInfo here is a combination of system ID followed by system age
@@ -177,7 +177,7 @@ In this application, you use a Spark [ML pipeline](https://spark.apache.org/docs
     Row(SystemInfo=u'7 22', prediction=0.0, probability=DenseVector([0.5015, 0.4985]))
     ```
 
-   From the first row in the prediction, you can see that for an HVAC system with ID 20 and system age of 25 years, the building is hot (**prediction=1.0**). The first value for DenseVector (0.49999) corresponds to the  prediction 0.0 and the second value (0.5001) corresponds to the prediction 1.0. In the output, even though the second value is only marginally higher, the model shows **prediction=1.0**.
+   Observe the first row in the prediction. For an HVAC system with ID 20 and system age of 25 years, the building is hot (**prediction=1.0**). The first value for DenseVector (0.49999) corresponds to the  prediction 0.0 and the second value (0.5001) corresponds to the prediction 1.0. In the output, even though the second value is only marginally higher, the model shows **prediction=1.0**.
 
 1. Shut down the notebook to release the resources. To do so, from the **File** menu on the notebook, select **Close and Halt**. This action shuts down and closes the notebook.
 
@@ -199,7 +199,7 @@ If you're not going to continue to use this application, delete the cluster that
 
 1. Select **Delete**. Select **Yes**.
 
-![Azure portal delete an HDInsight cluster](./media/apache-spark-ipython-notebook-machine-learning/hdinsight-azure-portal-delete-cluster.png "Delete HDInsight cluster")
+![Azure portal deletes an HDInsight cluster](./media/apache-spark-ipython-notebook-machine-learning/hdinsight-azure-portal-delete-cluster.png "Delete HDInsight cluster")
 
 ## Next steps