Merge pull request #159124 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to master to sync with https://github.com/MicrosoftDocs/azure-docs (branch master)

Author: huypub

articles/iot-hub/iot-hub-public-network-access.md

@@ -1,5 +1,5 @@
 ---
-title: Managing public network access for Azure IoT hub
+title: Managing public network access for Azure IoT Hub
 description: Documentation on how to disable and enable public network access for IoT hub
 author: jlian
 ms.author: jlian

articles/machine-learning/concept-automated-ml.md

@@ -129,7 +129,7 @@ In every automated machine learning experiment, your data is automatically scale
 | [StandardScaleWrapper](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)  | Standardize features by removing the mean and scaling to unit variance  |
 | [MinMaxScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)  | Transforms features by scaling each feature by that column's minimum and maximum  |
 | [MaxAbsScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler) |Scale each feature by its maximum absolute value |
-| [RobustScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html) |This Scaler features by their quantile range |
+| [RobustScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html) | Scales features by their quantile range |
 | [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) |Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space |
 | [TruncatedSVDWrapper](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) |This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition, which means it can work with scipy.sparse matrices efficiently |
 | [SparseNormalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) | Each sample (that is, each row of the data matrix) with at least one non-zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one |

articles/stream-analytics/on-azure-stack.md

@@ -1,6 +1,6 @@
 ---
 title: Run Azure Stream Analytics on Azure Stack
-description: Create an Azure Stream Analytics edge job and deploy it to Azure Stack hub via the IoT Edge runtime.
+description: Create an Azure Stream Analytics edge job and deploy it to Azure Stack Hub via the IoT Edge runtime.
 ms.service: stream-analytics
 author: an-emma
 ms.author: raan