Merge pull request #102351 from cjgronlund/algo-x-refs

Formatting & xrefs: algo ref

Author: PMEds28

articles/machine-learning/algorithm-cheat-sheet.md

@@ -15,7 +15,9 @@ ms.date: 11/04/2019
 
 The **Azure Machine Learning Algorithm Cheat Sheet** helps you choose the right algorithm for a predictive analytics model.
 
-Azure Machine Learning has a large library of algorithms from the ***classification***, ***recommender systems***, ***clustering***, ***anomaly detection***, ***regression*** and ***text analytics*** families. Each is designed to address a different type of machine learning problem.
+Azure Machine Learning has a large library of algorithms from the ***classification***, ***recommender systems***, ***clustering***, ***anomaly detection***, ***regression***, and ***text analytics*** families. Each is designed to address a different type of machine learning problem.
+
+For additional guidance, see [How to select algorithms](how-to-select-algorithms.md)
 
 ## Download: Machine Learning Algorithm Cheat Sheet
 
@@ -27,7 +29,7 @@ Download and print the Machine Learning Algorithm Cheat Sheet in tabloid size to
 
 ## How to use the Machine Learning Algorithm Cheat Sheet
 
-The suggestions offered in this algorithm cheat sheet are approximate rules-of-thumb. Some can be bent, and some can be flagrantly violated. This is intended to suggest a starting point. Don’t be afraid to run a head-to-head competition between several algorithms on your data. There is simply no substitute for understanding the principles of each algorithm and the system that generated your data.
+The suggestions offered in this algorithm cheat sheet are approximate rules-of-thumb. Some can be bent, and some can be flagrantly violated. This cheat sheet is intended to suggest a starting point. Don’t be afraid to run a head-to-head competition between several algorithms on your data. There is simply no substitute for understanding the principles of each algorithm and the system that generated your data.
 
 Every machine learning algorithm has its own style or inductive bias. For a specific problem, several algorithms may be appropriate, and one algorithm may be a better fit than others. But it's not always possible to know beforehand which is the best fit. In cases like these, several algorithms are listed together in the cheat sheet. An appropriate strategy would be to try one algorithm, and if the results are not yet satisfactory, try the others. 
 
@@ -43,17 +45,17 @@ In supervised learning, each data point is labeled or associated with a category
 
 ### Unsupervised learning
 
-In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping it into clusters, as K-means does, or finding different ways of looking at complex data so that it appears simpler.
+In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. Unsupervised learning groups data into clusters, as K-means does, or finds different ways of looking at complex data so that it appears simpler.
 
 ### Reinforcement learning
 
-In reinforcement learning, the algorithm gets to choose an action in response to each data point. It is a common approach in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot’s next action. It's also a natural fit for Internet of Things applications. The learning algorithm also receives a reward signal a short time later, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward. 
+In reinforcement learning, the algorithm gets to choose an action in response to each data point. It is a common approach in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot’s next action. It's also a natural fit for Internet of Things applications. The learning algorithm also receives a reward signal a short time later, indicating how good the decision was. Based on this signal, the algorithm modifies its strategy in order to achieve the highest reward. 
 
 ## Next steps
 
-* [Learn about studio in Azure Machine Learning and the Azure portal](overview-what-is-azure-ml.md).
+* See additional guidance on [How to select algorithms](how-to-select-algorithms.md)
 
-* See a list of algorithms and modules in the [Algorithm and module reference](algorithm-module-reference/module-reference.md).
+* [Learn about studio in Azure Machine Learning and the Azure portal](overview-what-is-azure-ml.md).
 
 * [Tutorial: Build a prediction model in Azure Machine Learning designer](tutorial-designer-automobile-price-train-score.md).
 

articles/machine-learning/algorithm-module-reference/module-reference.md

@@ -17,25 +17,35 @@ This reference content provides the technical background on each of the machine
 
 Each module represents a set of code that can run independently and perform a machine learning task, given the required inputs. A module might contain a particular algorithm, or perform a task that is important in machine learning, such as missing value replacement, or statistical analysis.
 
+For help with choosing algorithms, see 
+* [How to select algorithms](../how-to-select-algorithms.md)
+* [Azure Machine Learning Algorithm Cheat Sheet](../algorithm-cheat-sheet.md)
+
 > [!TIP]
 > In any pipeline in the designer, you can get information about a specific module. Select the module, then select the **more help** link in the **Quick Help** pane.
 
-## Modules
+## Data preparation modules
 
-Modules are organized by functionality:
 
 | Functionality | Description | Module |
 | --- |--- | --- |
-|  | **Data preparation**: | |
 | Data input and output | Move data from cloud sources into your pipeline. Write your results or intermediate data to Azure Storage, a SQL database, or Hive, while running a pipeline, or use cloud storage to exchange data between pipelines.  | [Enter Data Manually](enter-data-manually.md) <br/> [Export Data](export-data.md) <br/> [Import Data](import-data.md) |
 | Data transformation | Operations on data that are unique to machine learning, such as normalizing or binning data, dimensionality reduction, and converting data among various file formats.| [Add Columns](add-columns.md) <br/> [Add Rows](add-rows.md) <br/> [Apply Math Operation](apply-math-operation.md) <br/> [Apply SQL Transformation](apply-sql-transformation.md) <br/> [Clean Missing Data](clean-missing-data.md) <br/> [Clip Values](clip-values.md) <br/> [Convert to CSV](convert-to-csv.md) <br/> [Convert to Dataset](convert-to-dataset.md) <br/> [Edit Metadata](edit-metadata.md) <br/> [Join Data](join-data.md) <br/> [Normalize Data](normalize-data.md) <br/> [Partition and Sample](partition-and-sample.md)  <br/> [Remove Duplicate Rows](remove-duplicate-rows.md) <br/> [SMOTE](smote.md) <br/> [Select Columns Transform](select-columns-transform.md) <br/> [Select Columns in Dataset](select-columns-in-dataset.md) <br/> [Split Data](split-data.md) |
 | Feature Selection | Select a subset of relevant, useful features to use in building an analytical model. | [Filter Based Feature Selection](filter-based-feature-selection.md) <br/> [Permutation Feature Importance](permutation-feature-importance.md) |
 | Statistical Functions | Provide a wide variety of statistical methods related to data science. | [Summarize Data](summarize-data.md)|
-|  | **Machine learning algorithms**: | |
+
+## Machine learning algorithms
+
+| Functionality | Description | Module |
+| --- |--- | --- |
 | Regression | Predict a value. | [Boosted Decision Tree Regression](boosted-decision-tree-regression.md) <br/> [Decision Forest Regression](decision-forest-regression.md) <br/> [Linear Regression](linear-regression.md)  <br/> [Neural Network Regression](neural-network-regression.md)  <br/> |
 | Clustering | Group data together.| [K-Means Clustering](k-means-clustering.md)
 | Classification | Predict a class.  Choose from binary (two-class) or multiclass algorithms.| [Multiclass Boosted Decision Tree](multiclass-boosted-decision-tree.md) <br/> [Multiclass Decision Forest](multiclass-decision-forest.md) <br/> [Multiclass Logistic Regression](multiclass-logistic-regression.md)  <br/> [Multiclass Neural Network](multiclass-neural-network.md) <br/> [One vs. All Multiclass](one-vs-all-multiclass.md) <br/> [Two-Class Averaged Perceptron](two-class-averaged-perceptron.md) <br/>  [Two-Class Boosted Decision Tree](two-class-boosted-decision-tree.md)  <br/> [Two-Class Decision Forest](two-class-decision-forest.md) <br/>  [Two-Class Logistic Regression](two-class-logistic-regression.md) <br/> [Two-Class Neural Network](two-class-neural-network.md) <br/> [Two Class Support Vector Machine](two-class-support-vector-machine.md) | 
-|  | **Build and evaluate models**: | |
+
+## Modules for building and evaluating models
+
+| Functionality | Description | Module |
+| --- |--- | --- |
 | Model training | Run data through the algorithm. |  [Train Clustering Model](train-clustering-model.md) <br/> [Train Model](train-model.md)  <br/> [Tune Model Hyperparameters](tune-model-hyperparameters.md) |
 | Model Scoring and Evaluation | Measure the accuracy of the trained model. | [Apply Transformation](apply-transformation.md) <br/> [Assign Data to Clusters](assign-data-to-clusters.md) <br/> [Cross Validate Model](cross-validate-model.md) <br/> [Evaluate Model](evaluate-model.md) <br/> [Score Model](score-model.md) |
 | Python language | Write code and embed it in a module to integrate Python with your pipeline. | [Create Python Model](create-python-model.md) <br/> [Execute Python Script](execute-python-script.md) |
@@ -46,3 +56,7 @@ Modules are organized by functionality:
 ## Error messages
 
 Learn about the [error messages and exception codes](designer-error-codes.md) you might encounter using modules in Azure Machine Learning designer.
+
+## Next steps
+
+* [Tutorial: Build a model in designer to predict auto prices](../tutorial-designer-automobile-price-train-score.md)