Merge pull request #49638 from ShawnKupfer/WB1726

AB#1037347: Select and customize architectures and hyperparameters us…

Author: v-regandowner

learn-pr/azure/machine-learning-architectures-and-hyperparameters/1-introduction.yml

@@ -1,13 +1,15 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.introduction
-title: Introduction
-metadata:
-  title: Introduction
-  description: Introduction to the introduction to regression module.
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 2
-content: |
-  [!include[](includes/1-introduction.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.introduction
+title: Introduction
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Introduction
+  description: Introduction to the introduction to regression module.
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)] 

learn-pr/azure/machine-learning-architectures-and-hyperparameters/2-decision-trees.yml

@@ -1,13 +1,15 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.decision-trees
-title: Decision trees and model architecture
-metadata:
-  title: Decision trees and model architecture
-  description: Conceptual unit about decision trees and model architecture
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/2-decision-trees.md)]
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.decision-trees
+title: Decision trees and model architecture
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Decision trees and model architecture
+  description: Conceptual unit about decision trees and model architecture
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/2-decision-trees.md)]

learn-pr/azure/machine-learning-architectures-and-hyperparameters/3-exercise-decision-trees.yml

@@ -1,14 +1,16 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-decision-trees
-title: Exercise - Decision trees and model architecture
-metadata:
-  title: Exercise - Decision trees and model architecture
-  description: Learn how to build a simple model.
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 8
-sandbox: true
-notebook: notebooks/7-3-exercise-decision-trees.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-decision-trees
+title: Exercise - Decision trees and model architecture
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Exercise - Decision trees and model architecture
+  description: Learn how to build a simple model.
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 8
+sandbox: true
+notebook: notebooks/7-3-exercise-decision-trees.ipynb
+

learn-pr/azure/machine-learning-architectures-and-hyperparameters/4-random-forests.yml

@@ -1,14 +1,16 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.random-forests
-title: Random forests and selecting architectures
-metadata:
-  title: Random forests and selecting architectures
-  description: Conceptual unit about random forests and selecting architectures
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/4-random-forests.md)]
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.random-forests
+title: Random forests and selecting architectures
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Random forests and selecting architectures
+  description: Conceptual unit about random forests and selecting architectures
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/4-random-forests.md)]
+

learn-pr/azure/machine-learning-architectures-and-hyperparameters/5-exercise-random-forests.yml

@@ -1,14 +1,16 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-random-forests
-title: Exercise - Selecting random forest architectures
-metadata:
-  title: Exercise - Selecting random forest architectures
-  description: Exercise about selecting random forest architectures
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 10
-sandbox: true
-notebook: notebooks/7-5-exercise-random-forests.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-random-forests
+title: Exercise - Selecting random forest architectures
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Exercise - Selecting random forest architectures
+  description: Exercise about selecting random forest architectures
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 10
+sandbox: true
+notebook: notebooks/7-5-exercise-random-forests.ipynb
+

learn-pr/azure/machine-learning-architectures-and-hyperparameters/6-hyperparameters.yml

@@ -1,13 +1,15 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.hyperparameters
-title: Hyperparameters in classification
-metadata:
-  title: Hyperparameters in classification
-  description: Conceptual unit about hyperparameters in classification
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 6
-content: |
-  [!include[](includes/6-hyperparameters.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.hyperparameters
+title: Hyperparameters in classification
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Hyperparameters in classification
+  description: Conceptual unit about hyperparameters in classification
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/6-hyperparameters.md)] 

learn-pr/azure/machine-learning-architectures-and-hyperparameters/7-exercise-hyperparameters-tuning.yml

@@ -1,14 +1,16 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-hyperparameters-tuning
-title: Exercise - Hyperparameter tuning with random forests
-metadata:
-  title: Exercise - Hyperparameter tuning with random forests
-  description: Exercise about hyperparameter tuning with random forests
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 12
-sandbox: true
-notebook: notebooks/7-7-exercise-hyperparameters-tuning.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.exercise-hyperparameters-tuning
+title: Exercise - Hyperparameter tuning with random forests
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Exercise - Hyperparameter tuning with random forests
+  description: Exercise about hyperparameter tuning with random forests
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 12
+sandbox: true
+notebook: notebooks/7-7-exercise-hyperparameters-tuning.ipynb
+

learn-pr/azure/machine-learning-architectures-and-hyperparameters/8-knowledge-check.yml

@@ -1,48 +1,50 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.knowledge-check
-title: Knowledge check
-metadata:
-  title: Knowledge check
-  description: Multiple-choice questions
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-quiz:
-  title: Check your knowledge
-  questions:
-  - content: 'Decision trees are classification models that…'
-    choices:
-    - content: "Typically have excellent generalizability to test sets"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Can only make binary (true/false) predictions"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Typically overfit their training data"
-      isCorrect: true
-      explanation: "Correct."
-  - content: 'What does selecting model architectures mean?'
-    choices:
-    - content: "Picking a model’s parameter values before training"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Deciding how a model is structured"
-      isCorrect: true
-      explanation: "Correct."
-    - content: "Selecting a 32-bit or 64-bit compilation option"
-      isCorrect: false
-      explanation: "Incorrect."
-  - content: 'Hyperparameter selection can help give optimal models and…'
-    choices:
-    - content: "Refers to selecting values that change how the learning process works"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Often requires experimentation to get right"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "All of the above"
-      isCorrect: true
-      explanation: "Correct."
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.knowledge-check
+title: Knowledge check
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Knowledge check
+  description: Multiple-choice questions
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+quiz:
+  title: Check your knowledge
+  questions:
+  - content: 'Decision trees are classification models that…'
+    choices:
+    - content: "Typically have excellent generalizability to test sets"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Can only make binary (true/false) predictions"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Typically overfit their training data"
+      isCorrect: true
+      explanation: "Correct."
+  - content: 'What does selecting model architectures mean?'
+    choices:
+    - content: "Picking a model's parameter values before training"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Deciding how a model is structured"
+      isCorrect: true
+      explanation: "Correct."
+    - content: "Selecting a 32-bit or 64-bit compilation option"
+      isCorrect: false
+      explanation: "Incorrect."
+  - content: 'Hyperparameter selection can help give optimal models and…'
+    choices:
+    - content: "Refers to selecting values that change how the learning process works"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Often requires experimentation to get right"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "All of the above"
+      isCorrect: true
+      explanation: "Correct."
+

learn-pr/azure/machine-learning-architectures-and-hyperparameters/9-summary.yml

@@ -1,13 +1,15 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.summary
-title: Summary
-metadata:
-  title: Summary
-  description: An overview of the content covered in the module.
-  ms.date: 07/21/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-content: |
-  [!include[](includes/9-summary.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.machine-learning-architectures-and-hyperparameters.summary
+title: Summary
+metadata:
+  adobe-target: true
+  prefetch-feature-rollout: true
+  title: Summary
+  description: An overview of the content covered in the module.
+  ms.date: 03/24/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+content: |
+  [!include[](includes/9-summary.md)] 

learn-pr/azure/machine-learning-architectures-and-hyperparameters/includes/1-introduction.md

@@ -1,10 +1,10 @@
-Not all models are simple mathematical equations that can be plotted as a line. Instead, some complex models are easier thought of more like flow charts or traditional programming structures. Such models usually have extra levels of customization available, which can make them more powerful, though also trickier to work with. Throughout these exercises, we'll explore this by manipulating how models work and are trained. Although we'll focus on one type of model, the general principles taught here apply to many other model types as well.
+Not all models are simple mathematical equations that can be plotted as a line. Instead, it's easier to think of some complex models as more like flow charts or traditional programming structures. Such models usually have extra levels of customization available, which can make them more powerful, but also trickier to work with. This module explores this concept by manipulating how models work and how they're trained. Although we'll focus on one type of model, the general principles we cover here apply to many other model types as well.
 
 ## Scenario: Predicting sports results using machine learning
 
-Throughout this module, we'll refer to the following example scenario as we explain concepts surrounding model architecture and hyperparameters. This scenario is designed to appear complex at first, but as the exercises progress we'll learn how you can tackle it using a little critical thinking and experimentation.
+Throughout this module, we'll refer to the following example scenario as we explain concepts surrounding model architecture and hyperparameters. This scenario is designed to appear complex at first, but as the exercises progress, we'll learn how you can tackle it using a little critical thinking and experimentation.
 
-The Games' motto consists of three Latin words: Citius - Altius - Fortius. These words mean Faster - Higher - Stronger. Since this motto was established, the variety of games has grown enormously to include shooting, sailing, and team sports. We'd like to explore the role that basic physical features still play in predicting who wins a medal at one of the most prestigious sporting events on the planet. To this end, we'll explore rhythmic gymnastics: a modern addition to the games that combines dance, gymnastics, and calisthenics. One might expect that basic characteristics of age, height, and weight play only a limited role, given the need for agility, flexibility, dexterity, and coordination. Let's use some more advanced machine learning models to see how critical these basic factors really are.
+The Olympic Games' motto consists of three Latin words: *Citius*, *Altius*, *Fortius*. These words mean *Faster, Higher, Stronger*. Since this motto was established, the variety of games has grown enormously to include shooting, sailing, and team sports. We'd like to explore the role that basic physical features still play in predicting who wins a medal at one of the most prestigious sporting events on the planet. To this end, we'll explore rhythmic gymnastics: a modern addition to the games that combines dance, gymnastics, and calisthenics. One might expect that basic characteristics of age, height, and weight play only a limited role, given the need for agility, flexibility, dexterity, and coordination. Let's use some more advanced machine learning models to see how critical these basic factors really are.
 
 ## Prerequisites