Writing

Author: PeterCLu

articles/machine-learning/samples-designer.md

@@ -9,26 +9,23 @@ ms.topic: sample
 
 author: peterclu
 ms.author: peterlu
-ms.date: 03/06/2020
+ms.date: 03/10/2020
 ---
 # Designer sample pipelines
 
 Use the built-in example in Azure Machine Learning designer to quickly get started building your own machine learning pipelines.
 
-The Azure Machine Learning designer [GitHub repository](https://github.com/Azure/MachineLearningDesigner) contains the latest pipeline samples to help you get started with common machine learning scenarios. This article shows the following:
-
-- Instructions for using designer samples
-- Links to the sample pipeline documentation
+The Azure Machine Learning designer [GitHub repository](https://github.com/Azure/MachineLearningDesigner) contains the latest pipeline samples to help you get started with common machine learning scenarios.
 
 ## Prerequisites
 
 * An Azure subscription. If you don't have an Azure subscription, create a [free account](https://aka.ms/AMLFree).
 * An Azure Machine Learning workspace with the Enterprise SKU.
 
 
-## How to use the sample pipelines
+## How to use sample pipelines
 
-Use the sample pipelines as a starting point to quickly get started with common machine learning scenarios.
+The designer saves a copy of the sample pipelines to your studio workspace. You can edit the pipeline to adapt it to your needs and save it as your own. Use them as a starting point to jumpstart your projects.
 
 1. Sign in to <a href="https://ml.azure.com?tabs=jre" target="_blank">ml.azure.com</a>, and select the workspace you want to work with.
 
@@ -38,47 +35,45 @@ Use the sample pipelines as a starting point to quickly get started with common
 
     Select **Show more samples** for a complete list of samples.
 
-The designer saves a copy of the sample to your studio workspace. You can edit the pipeline to adapt it to your needs and save it as your own. 
-
 ## Regression samples
 
 Learn more about the built-in regression samples.
 
 | Sample title | Description | 
 | --- | --- |
-| [Sample 1: Regression - Automobile Price Prediction (Basic)]() | Predict car prices using linear regression. |
-| [Sample 2: Regression - Automobile Price Prediction (Advanced)]() | Predict car prices using decision forest and boosted decision tree regressors. Compare models to find the best algorithm.
+| [Sample 1: Regression - Automobile Price Prediction (Basic)](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-regression-automobile-price-basic.md) | Predict car prices using linear regression. |
+| [Sample 2: Regression - Automobile Price Prediction (Advanced)](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-regression-automobile-price-compare-algorithms.md) | Predict car prices using decision forest and boosted decision tree regressors. Compare models to find the best algorithm.
 
 ## Classification samples
 
-Learn more about the built-in classification samples.
+Learn more about the built-in classification samples. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
 
 | Sample title | Description | 
 | --- | --- |
-| [Sample 3: Binary Classification with Feature Selection - Income Prediction]() | Predict income as high or low, using a two-class boosted decision tree. Use Pearson correlation to select features.
-| [Sample 4: Binary Classification with custom Python script - Credit Risk Prediction]() | Classify credit applications as high or low risk. Use the Execute Python Script module to weight your data.
-| [Sample 5: Binary Classification - Customer Relationship Prediction]() | Predict customer churn using two-class boosted decision trees. Use SMOTE to sample biased data.
-| [Sample 7: Text Classification - Wikipedia SP 500 Dataset]() | Classify company types from Wikipedia articles with multiclass logistic regression. |
-| [Sample 12: Multiclass Classification - Letter Recognition]() | Create an ensemble of binary classifiers to classify written letters. |
+| [Sample 3: Binary Classification with Feature Selection - Income Prediction](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-classification-predict-income.md) | Predict income as high or low, using a two-class boosted decision tree. Use Pearson correlation to select features.
+| [Sample 4: Binary Classification with custom Python script - Credit Risk Prediction](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-classification-credit-risk-cost-sensitive.md) | Classify credit applications as high or low risk. Use the Execute Python Script module to weight your data.
+| [Sample 5: Binary Classification - Customer Relationship Prediction](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-classification-churn.md) | Predict customer churn using two-class boosted decision trees. Use SMOTE to sample biased data.
+| [Sample 7: Text Classification - Wikipedia SP 500 Dataset](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-text-classification.md) | Classify company types from Wikipedia articles with multiclass logistic regression. |
+| Sample 12: Multiclass Classification - Letter Recognition | Create an ensemble of binary classifiers to classify written letters. |
 
 ## Recommender samples
 
-Learn more about the built-in recommender samples.
+Learn more about the built-in recommender samples. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
 
 | Sample title | Description | 
 | --- | --- |
-| [Sample 10: Recommendation - Movie Rating Tweets]() | Build a movie recommender engine from movie titles and rating. |
+| Sample 10: Recommendation - Movie Rating Tweets | Build a movie recommender engine from movie titles and rating. |
 
 ## Utility samples
 
-Learn more about the samples that demonstrate machine learning utilities and features.
+Learn more about the samples that demonstrate machine learning utilities and features. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
 
 | Sample title | Description | 
 | --- | --- |
-| [Sample 6: Use custom R script - Flight Delay Prediction]() |
-| [Sample 8: Cross Validation for Binary Classification - Adult Income Prediction]() | Use cross validation to build a binary classifier for adult income.
-| [Sample 9: Permutation Feature Importance]() | Use permutation feature importance to compute importance scores for the test dataset. 
-| [Sample 11: Tune Parameters for Binary Classification - Adult Income Prediction]() | Use Tune Model Hyperparameters to find optimal hyperparameters to build a binary classifier. |
+| [Sample 6: Use custom R script - Flight Delay Prediction](https://github.com/Azure/MachineLearningDesigner/blob/master/articles/samples/how-to-designer-sample-classification-flight-delay.md) |
+| Sample 8: Cross Validation for Binary Classification - Adult Income Prediction | Use cross validation to build a binary classifier for adult income.
+| Sample 9: Permutation Feature Importance | Use permutation feature importance to compute importance scores for the test dataset. 
+| Sample 11: Tune Parameters for Binary Classification - Adult Income Prediction | Use Tune Model Hyperparameters to find optimal hyperparameters to build a binary classifier. |
 
 ## Clean up resources