Update concept-automated-ml.md

Author: j-martens

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

@@ -45,7 +45,7 @@ See examples of regression and automated machine learning for predictions in the
 
 ### 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. Learn more with this how-to: [automated machine learning for time series forecasting](how-to-auto-train-forecast.md). 
 
 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.
 
@@ -57,7 +57,6 @@ Advanced forecasting configuration includes:
 * configurable lags
 * rolling window aggregate features
 
-Learn more with this how-to: [automated machine learning for time series forecasting](how-to-auto-train-forecast.md). 
 
 See examples of regression and automated machine learning for predictions in these Python notebooks: [Sales Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb), [Demand Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb), and [Beverage Production Forecast](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb).
 
@@ -69,13 +68,14 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
 
 1. **Identify the ML problem** to be solved: classification, forecasting, or regression
 
-1. **Choose whether you want to use the Python SDK or the studio web experience**.
+1. **Choose whether you want to use the Python SDK or the studio web experience**:
+   Learn about the parity between the [Python SDK and studio web experience](#parity).
 
    * For limited or no code experience, try the Azure Machine Learning studio web experience at [https://ml.azure.com](https://ml.azure.com/)  
    * For Python developers, check out the [Azure Machine Learning Python SDK](how-to-configure-auto-train.md) 
 
-    [!INCLUDE [aml-applies-to-enterprise-sku](../../includes/aml-applies-to-enterprise-sku-inline.md)]  Learn about the parity between the [Python SDK and studio web experience](#parity).
-
+    [!INCLUDE [aml-applies-to-enterprise-sku](../../includes/aml-applies-to-enterprise-sku-inline.md)]  
+    
 1. **Specify the source and format of the labeled training data**: Numpy arrays or Pandas dataframe
 
 1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks](how-to-set-up-training-targets.md).  Learn about automated training [on a remote resource](how-to-auto-train-remote.md).