Merge pull request #107194 from nibaccam/automl-refresh

AutoML | How to for UI file rename

Author: Jak-MS

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

@@ -84,7 +84,7 @@ In every automated machine learning experiment, your data is automatically scale
 |Scaling & normalization| Description |
 | ------------- | ------------- |
 | [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  |
+| [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 |
 | [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 |
@@ -93,10 +93,10 @@ In every automated machine learning experiment, your data is automatically scale
 
 ### Advanced preprocessing: optional featurization
 
-Additional advanced preprocessing and featurization are also available, such as data guardrails, encoding, and transforms. [Learn more about what featurization is included](how-to-create-portal-experiments.md#featurization). 
+Additional advanced preprocessing and featurization are also available, such as data guardrails, encoding, and transforms. [Learn more about what featurization is included](how-to-use-automated-ml-for-ml-models.md#featurization). 
 Enable this setting with:
 
-+ Azure Machine Learning studio: Enable **Automatic featurization** in the **View additional configuration** section [with these steps](how-to-create-portal-experiments.md#create-and-run-experiment).
++ Azure Machine Learning studio: Enable **Automatic featurization** in the **View additional configuration** section [with these steps](how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment).
 
 + Python SDK: Specifying `"feauturization": 'auto' / 'off' / 'FeaturizationConfig'` for the [`AutoMLConfig` class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig). 
 
@@ -153,17 +153,17 @@ Model **C** represents a clear case of over-fitting; the training accuracy is ve
 
 ## Classification & regression
 
-Classification and regression are the most common types of machine learning tasks. Both are types of supervised learning in which models learn using training data, and apply those learnings to new data. Azure Machine Learning offers featurizations specifically for these tasks, such as deep neural network text featurizers for classification. Learn more about [featurization options](how-to-create-portal-experiments.md#featurization). 
+Classification and regression are the most common types of machine learning tasks. Both are types of supervised learning in which models learn using training data, and apply those learnings to new data. Azure Machine Learning offers featurizations specifically for these tasks, such as deep neural network text featurizers for classification. Learn more about [featurization options](how-to-use-automated-ml-for-ml-models.md#featurization). 
 
 The main goal of classification models is to predict which categories new data will fall into based on learnings from its training data. Common classification examples include fraud detection, handwriting recognition, and object detection.  Learn more and see an example of [classification with automated machine learning](tutorial-train-models-with-aml.md).
 
 Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating, etc. Learn more and see an example of [regression with automated machine learning](tutorial-auto-train-models.md).
 
 ## Time-series forecasting
 
-Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast.
+Building forecasts is an integral part of any business, whether it's revenue, inventory, sales, or customer demand. You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast.
 
-An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are “pivoted” to become additional dimensions for the regressor together with other predictors. This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible.
+An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are "pivoted" to become additional dimensions for the regressor together with other predictors. This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible.
 
 Learn more and see an example of [automated machine learning for time series forecasting](how-to-auto-train-forecast.md). Or, see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) for detailed code examples of advanced forecasting configuration including:
 
@@ -191,7 +191,7 @@ Imbalanced data is commonly found in data for machine learning classification sc
 
 As part of its goal of simplifying the machine learning workflow, automated ML has built in capabilities to help deal with imbalanced data such as, 
 
-- A **weight column**: automated ML supports a weighted column as input, causing rows in the data to be weighted up or down, which can make a class more or less “important”.
+- A **weight column**: automated ML supports a weighted column as input, causing rows in the data to be weighted up or down, which can make a class more or less "important".
 
 - The algorithms used by automated ML can properly handle imbalance of up to 20:1, meaning the most common class can have 20 times more rows in the data than the least common class.
 
@@ -213,7 +213,7 @@ The following techniques are additional options to handle imbalanced data outsid
 
 - Resampling to even the class imbalance, either by up-sampling the smaller classes or down-sampling the larger classes. These methods require expertise to process and analyze.
 
-- Use a performance metric that deals better with imbalanced data. For example, the F1 score is a weighted average of precision and recall. Precision measures a classifier’s exactness-- low precision indicates a high number of false positives--, while recall measures a classifier’s completeness-- low recall indicates a high number of false negatives. 
+- Use a performance metric that deals better with imbalanced data. For example, the F1 score is a weighted average of precision and recall. Precision measures a classifier's exactness-- low precision indicates a high number of false positives--, while recall measures a classifier's completeness-- low recall indicates a high number of false negatives. 
 
 ## Use with ONNX in C# apps
 
@@ -286,7 +286,7 @@ See examples and learn how to build models using automated machine learning:
 + Follow the [Tutorial: Automatically train a regression model with Azure Machine Learning](tutorial-auto-train-models.md)
 
 + Configure the settings for automatic training experiment:
-  + In Azure Machine Learning studio, [use these steps](how-to-create-portal-experiments.md).
+  + In Azure Machine Learning studio, [use these steps](how-to-use-automated-ml-for-ml-models.md).
   + With the Python SDK, [use these steps](how-to-configure-auto-train.md).
 
 + Learn how to auto train using time series data, [use these steps](how-to-auto-train-forecast.md).

articles/machine-learning/concept-data.md

@@ -79,7 +79,7 @@ With datasets, you can accomplish a number of machine learning tasks through sea
 
 + [Train machine learning models](how-to-train-with-datasets.md).
 + Consume datasets in 
-     + [automated ML experiments](how-to-create-portal-experiments.md)
+     + [automated ML experiments](how-to-use-automated-ml-for-ml-models.md)
      + the [designer](tutorial-designer-automobile-price-train-score.md#import-data) 
 + Access datasets for scoring with batch inference in [machine learning pipelines](how-to-create-your-first-pipeline.md).
 + Create a [data labeling project](#label).

articles/machine-learning/concept-train-machine-learning-model.md

@@ -64,7 +64,7 @@ Define the iterations, hyperparameter settings, featurization, and other setting
 * [Examples: Jupyter Notebook examples for automated machine learning](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning)
 * [How to: Configure automated ML experiments in Python](how-to-configure-auto-train.md)
 * [How to: Autotrain a time-series forecast model](how-to-auto-train-forecast.md)
-* [How to: Create, explore, and deploy automated machine learning experiments with [Azure Machine Learning studio](how-to-create-portal-experiments.md)
+* [How to: Create, explore, and deploy automated machine learning experiments with Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md)
 
 ### Estimators
 

articles/machine-learning/how-to-configure-auto-train.md

@@ -30,7 +30,7 @@ Configuration options available in automated machine learning:
 * Explore model metrics
 * Register and deploy model
 
-If you prefer a no code experience, you can also [Create your automated machine learning experiments in Azure Machine Learning studio](how-to-create-portal-experiments.md).
+If you prefer a no code experience, you can also [Create your automated machine learning experiments in Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md).
 
 ## Select your experiment type
 
@@ -169,7 +169,7 @@ Some examples include:
 
 The three different `task` parameter values (the third task-type is `forecasting`, and uses a similar algorithm pool as `regression` tasks) determine the list of models to apply. Use the `whitelist` or `blacklist` parameters to further modify iterations with the available models to include or exclude. The list of supported models can be found on [SupportedModels Class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels) for ([Classification](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification), [Forecasting](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting), and [Regression](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression)).
 
-Automated ML's validation serivce will require that `experiment_timeout_minutes` be set to a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
+Automated ML's validation service will require that `experiment_timeout_minutes` be set to a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
 
 ### Primary Metric
 The primary metric determines the metric to be used during model training for optimization. The available metrics you can select is determined by the task type you choose, and the following table shows valid primary metrics for each task type.
@@ -186,15 +186,15 @@ Learn about the specific definitions of these metrics in [Understand automated m
 
 ### Data featurization
 
-In every automated machine learning experiment, your data is [automatically scaled and normalized](concept-automated-ml.md#preprocess) to help *certain* algorithms that are sensitive to features that are on different scales.  However, you can also enable additional featurization, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-create-portal-experiments.md#featurization).
+In every automated machine learning experiment, your data is [automatically scaled and normalized](concept-automated-ml.md#preprocess) to help *certain* algorithms that are sensitive to features that are on different scales.  However, you can also enable additional featurization, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-use-automated-ml-for-ml-models.md#featurization).
 
 When configuring your experiments, you can enable the advanced setting `featurization`. The following table shows the accepted settings for featurization in the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
 
 |Featurization Configuration | Description |
 | ------------- | ------------- |
 |`"featurization":` `'FeaturizationConfig'`| Indicates customized featurization step should be used. [Learn how to customize featurization](how-to-configure-auto-train.md#customize-feature-engineering).|
 |`"featurization": 'off'`| Indicates featurization step should not be done automatically.|
-|`"featurization": 'auto'`| Indicates that as part of preprocessing, [data guardrails and featurization steps](how-to-create-portal-experiments.md#advanced-featurization-options) are performed automatically.|
+|`"featurization": 'auto'`| Indicates that as part of preprocessing, [data guardrails and featurization steps](how-to-use-automated-ml-for-ml-models.md#advanced-featurization-options) are performed automatically.|
 
 > [!NOTE]
 > Automated machine learning featurization steps (feature normalization, handling missing data,

articles/machine-learning/how-to-create-register-datasets.md

@@ -210,7 +210,7 @@ To create a dataset in the studio:
 1. Select **Tabular** or **File** for Dataset type.
 1. Select **Next** to open the **Datastore and file selection** form. On this form you select where to keep your dataset after creation, as well as select what data files to use for your dataset. 
 1. Select **Next** to populate the **Settings and preview** and **Schema** forms; they are intelligently populated based on file type and you can further configure your dataset prior to creation on these forms. 
-1. Select **Next** to review the **Confirm details** form. Check your selections and create an optional data profile for your dataset. Learn more about [data profiling](how-to-create-portal-experiments.md#profile). 
+1. Select **Next** to review the **Confirm details** form. Check your selections and create an optional data profile for your dataset. Learn more about [data profiling](how-to-use-automated-ml-for-ml-models.md#profile). 
 1. Select **Create** to complete your dataset creation.
 
 ## Register datasets