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learn-pr/azure/understand-classification-machine-learning/1-introduction.yml

@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.introduction
-title: Introduction
-metadata:
-  title: Introduction
-  description: Introduction to the introduction to regression module.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 2
-content: |
-  [!include[](includes/1-introduction.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: Introduction to the introduction to regression module.
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)] 

learn-pr/azure/understand-classification-machine-learning/2-what-is-classification.yml

@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.what-is-classification
-title: What are classification models?
-metadata:
-  title: What are classification models?
-  description: Conceptual unit introducing what classification models are
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/2-what-is-classification.md)]
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.what-is-classification
+title: What are classification models?
+metadata:
+  title: What are classification models?
+  description: Conceptual unit introducing what classification models are
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/2-what-is-classification.md)]

learn-pr/azure/understand-classification-machine-learning/3-exercise-build-regression-model.yml

@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.exercise-build-regression-model
-title: Exercise - Build a simple logistic regression model
-metadata:
-  title: Exercise - Build a simple logistic regression model
-  description: Learn how to build a simple logistic regression model.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 8
-sandbox: true
-notebook: notebooks/6-3-exercise-build-regression-model.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.exercise-build-regression-model
+title: Exercise - Build a simple logistic regression model
+metadata:
+  title: Exercise - Build a simple logistic regression model
+  description: Learn how to build a simple logistic regression model.
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 8
+sandbox: true
+notebook: notebooks/6-3-exercise-build-regression-model.ipynb
+

learn-pr/azure/understand-classification-machine-learning/4-assess-classification.yml

@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.assess-classification
-title: Assessing a classification model
-metadata:
-  title: Assessing a classification model
-  description: Conceptual unit about assessing a classification model
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/4-assess-classification.md)]
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.assess-classification
+title: Assessing a classification model
+metadata:
+  title: Assessing a classification model
+  description: Conceptual unit about assessing a classification model
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/4-assess-classification.md)]
+

learn-pr/azure/understand-classification-machine-learning/5-exercise-assess-regression-model.yml

@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.exercise-assess-regression-model
-title: Exercise - Assessing a logistic regression model
-metadata:
-  title: Exercise - Assessing a logistic regression model
-  description: Exercise unit learning to assess a logistic regression model
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 10
-sandbox: true
-notebook: notebooks/6-5-exercise-assess-regression-model.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.exercise-assess-regression-model
+title: Exercise - Assessing a logistic regression model
+metadata:
+  title: Exercise - Assessing a logistic regression model
+  description: Exercise unit learning to assess a logistic regression model
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 10
+sandbox: true
+notebook: notebooks/6-5-exercise-assess-regression-model.ipynb
+

learn-pr/azure/understand-classification-machine-learning/6-improve-classification.yml

@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.improve-classification
-title: Improving classification models
-metadata:
-  title: Improving classification models
-  description: Conceptual unit about improving classification models in machine learning
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 6
-content: |
-  [!include[](includes/6-improve-classification.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.improve-classification
+title: Improving classification models
+metadata:
+  title: Improving classification models
+  description: Conceptual unit about improving classification models in machine learning
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/6-improve-classification.md)] 

learn-pr/azure/understand-classification-machine-learning/7-exercise-improve-classification.yml

@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.exercise-improve-classification
-title: Exercise - Improving classification models
-metadata:
-  title: Exercise - Improving classification models
-  description: Exercise about improving classification models in machine learning
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 12
-sandbox: true
-notebook: notebooks/6-7-exercise-improve-classification.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.exercise-improve-classification
+title: Exercise - Improving classification models
+metadata:
+  title: Exercise - Improving classification models
+  description: Exercise about improving classification models in machine learning
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 12
+sandbox: true
+notebook: notebooks/6-7-exercise-improve-classification.ipynb
+

learn-pr/azure/understand-classification-machine-learning/8-knowledge-check.yml

@@ -1,48 +1,48 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.knowledge-check
-title: Module assessment
-metadata:
-  title: Module assessment
-  description: Multiple-choice questions
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-quiz:
-  title: Check your knowledge
-  questions:
-  - content: 'What is the difference between classical regression and classification models?'
-    choices:
-    - content: "Regression models provide labels such as ‘cherry’/’banana’ but classification models calculate continuous numbers"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Classification models and linear regression models are two names for the same thing"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Classification models provide labels such as ‘cherry’/’banana’ but regression models calculate continuous numbers"
-      isCorrect: true
-      explanation: "Correct."
-  - content: 'How can we improve real-world model performance?'
-    choices:
-    - content: "By adding features"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Both adding and removing features can be useful, depending on the situation"
-      isCorrect: true
-      explanation: "Correct."
-    - content: "By removing features"
-      isCorrect: false
-      explanation: "Incorrect."
-  - content: 'What is one reason why logistic regression uses log-loss rather than a more intuitive cost function?'
-    choices:
-    - content: "Log-loss is stricter about model errors, even if they're close to correct"
-      isCorrect: true
-      explanation: "Correct."
-    - content: "It's the only way to calculate error for categorical labels"
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "Log-loss is more permissive to model error if that error is close to correct"
-      isCorrect: false
-      explanation: "Incorrect."
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.knowledge-check
+title: Module assessment
+metadata:
+  title: Module assessment
+  description: Multiple-choice questions
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+quiz:
+  title: Check your knowledge
+  questions:
+  - content: "What's the difference between classical regression and classification models?"
+    choices:
+    - content: "Regression models provide labels such as 'cherry/'banana' but classification models calculate continuous numbers"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Classification models and linear regression models are two names for the same thing"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Classification models provide labels such as 'cherry'/'banana' but regression models calculate continuous numbers"
+      isCorrect: true
+      explanation: "Correct."
+  - content: 'How can we improve real-world model performance?'
+    choices:
+    - content: "By adding features"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Both adding and removing features can be useful, depending on the situation"
+      isCorrect: true
+      explanation: "Correct."
+    - content: "By removing features"
+      isCorrect: false
+      explanation: "Incorrect."
+  - content: 'What is one reason why logistic regression uses log-loss rather than a more intuitive cost function?'
+    choices:
+    - content: "Log-loss is stricter about model errors, even if they're close to correct"
+      isCorrect: true
+      explanation: "Correct."
+    - content: "It's the only way to calculate error for categorical labels"
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "Log-loss is more permissive to model error if that error is close to correct"
+      isCorrect: false
+      explanation: "Incorrect."
+

learn-pr/azure/understand-classification-machine-learning/9-summary.yml

@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.understand-classification-machine-learning.summary
-title: Summary
-metadata:
-  title: Summary
-  description: An overview of the content covered in the module.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-content: |
-  [!include[](includes/9-summary.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.understand-classification-machine-learning.summary
+title: Summary
+metadata:
+  title: Summary
+  description: An overview of the content covered in the module.
+  ms.date: 05/12/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+content: |
+  [!include[](includes/9-summary.md)] 

learn-pr/azure/understand-classification-machine-learning/includes/1-introduction.md

@@ -1,13 +1,13 @@
-Classification models’ outputs are categorical, meaning they can be used for labeling inputs or decision making. For example, a self-driving car uses classification to decide whether to turn left or right at a fork in the road. A classification model is different from classical regression models where outputs are continuous, such as the size of a shoe or the speed of a train. Classification models are diverse in how they work. To get started, let's focus on logistic regression, which is a simpler and popular type of model that is used extensively across many arms of science and industry.
+Classification models' outputs are categorical, meaning they can be used for labeling inputs or decision making. For example, a self-driving car uses classification to decide whether to turn left or right at a fork in the road. A classification model is different from classical regression models where outputs are continuous, such as the size of a shoe or the speed of a train. Classification models are diverse in how they work. To get started, let's focus on logistic regression, which is a simpler and popular type of model that is used extensively across many arms of science and industry.
 
 ## Scenario: Predicting avalanches with machine learning
 
 Throughout this module, we use the following example scenario to explain concepts related to classification. This scenario is designed to provide an example for how you might meet these concepts in your own programming.
 
-Your charity is responsible for avalanche-rescue operations at hiking trails across the northwest of the United States. Granted, the safest option would be to permanently close all trails during skiing and hiking season but that would mean no sportspeople would get to enjoy the great outdoors! Your goal is to build a model that can predict whether an individual day is likely to result in an avalanche. Then by using that prediction, you can close the trail when the risk is high. Keep in mind as you make predictions: Predicting avalanches that don't happen can hurt local tourism while failing to predict avalanches that do happen can result in loss of life. Clearly, a balance must be found.
+Your charity is responsible for avalanche-rescue operations at hiking trails across the northwest United States. Granted, the safest option would be to permanently close all trails during skiing and hiking season, but that would mean no sportspeople would get to enjoy the great outdoors! Your goal is to build a model that can predict whether an individual day is likely to result in an avalanche. Then, by using that prediction, you can close the trail when the risk is high. Keep in mind as you make predictions: predicting avalanches that don't happen can hurt local tourism, while failing to predict avalanches that do happen can result in loss of life. Clearly, you must find a balance.
 
 > [!CAUTION]
-> The data for these exercises are fabricated and are solely for educational purposes. For those eager hikers and skiers out there: Machine learning can be used for avalanche prediction but don’t use this data or your trained model for anything except learning about machine learning.
+> The data for these exercises is fabricated and solely for educational purposes. For those eager hikers and skiers out there: you can use machine learning for avalanche prediction, but don't use this data or your trained model for anything except learning about machine learning.
 
 ## Prerequisites
 
@@ -17,6 +17,6 @@ Your charity is responsible for avalanche-rescue operations at hiking trails acr
 
 In this module, you will:
 
-* Discover how classification differs from classical regression
-* Build models that can perform classification tasks
-* Explore how to assess and improve classification models
+* Discover how classification differs from classical regression.
+* Build models that can perform classification tasks.
+* Explore how to assess and improve classification models.