Merge pull request #54006 from sherzyang/main

JamesJBarnett · web-flow · commit 8a4b32e2eba1 · 2026-03-27T15:58:51.000-07:00
Update videos to latest avatars.
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/1-introduction.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/12-summary.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/12-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/2-what-is-machine-learning.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/2-what-is-machine-learning.yml
@@ -4,7 +4,7 @@ title: Machine learning models
 metadata:
   title: Machine learning models
   description: "What are machine learning models?"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/3-types-of-machine-learning.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/3-types-of-machine-learning.yml
@@ -4,7 +4,7 @@ title: Types of machine learning model
 metadata:
   title: Types of machine learning model
   description: "Compare types of machine learning"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/4-regression.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/4-regression.yml
@@ -4,7 +4,7 @@ title: Regression
 metadata:
   title: Regression
   description: "Regression"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/5-binary-classification.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/5-binary-classification.yml
@@ -4,7 +4,7 @@ title: Binary classification
 metadata:
   title: Binary classification
   description: "Binary classification"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/6-multiclass-classification.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/6-multiclass-classification.yml
@@ -4,7 +4,7 @@ title: Multiclass classification
 metadata:
   title: Multiclass classification
   description: "Multiclass classification"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/7-clustering.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/7-clustering.yml
@@ -4,7 +4,7 @@ title: Clustering
 metadata:
   title: Clustering
   description: "Clustering"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/8-deep-learning.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/8-deep-learning.yml
@@ -4,7 +4,7 @@ title: Deep learning
 metadata:
   title: Deep learning
   description: "Deep learning"
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   author: wwlpublish
   ms.author: gmalc
   ms.topic: unit
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/1-introduction.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/1-introduction.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=763fab04-8a49-48d6-b4fb-95cf5ec1720c]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=38937f79-512c-4a98-be25-e6bae6ae2511]
 
 ::: zone-end
 
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/12-summary.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/12-summary.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=f65fb738-637b-4592-b892-d9f9dfe1e1b6]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=5987da8e-ac5f-451e-a144-8a70350beac0]
 
 ::: zone-end
 
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/2-what-is-machine-learning.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/2-what-is-machine-learning.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=c997e945-2453-46d8-a26d-f09e6191df69]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=cb52f6e4-10c2-430e-9e0d-954ede6e03d6]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
@@ -23,11 +23,11 @@ Let's explore the steps involved in training and inferencing.
     - In the ice cream sales scenario, our goal is to train a model that can predict the number of ice cream sales based on the weather. The weather measurements for the day (temperature, rainfall, windspeed, and so on) would be the *features* (***x***), and the number of ice creams sold on each day would be the *label* (***y***).
     - In the medical scenario, the goal is to predict whether or not a patient is at risk of diabetes based on their clinical measurements. The patient's measurements (weight, blood glucose level, and so on) are the *features* (***x***), and the likelihood of diabetes (for example, *1* for at risk, *0* for not at risk) is the *label* (***y***).
     - In the Antarctic research scenario, we want to predict the species of a penguin based on its physical attributes. The key measurements of the penguin (length of its flippers, width of its bill, and so on) are the *features* (***x***), and the species (for example, *0* for Adelie, *1* for Gentoo, or *2* for Chinstrap) is the *label* (***y***).
-1. An *algorithm* is applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed on ***x*** to calculate ***y***. The specific algorithm used depends on the kind of predictive problem you're trying to solve (more about this later), but the basic principle is to try to *fit* the data to a function in which the values of the features can be used to calculate the label.
-1. The result of the algorithm is a *model* that encapsulates the calculation derived by the algorithm as a *function* - let's call it ***f***. In mathematical notation:
+2. An *algorithm* is applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed on ***x*** to calculate ***y***. The specific algorithm used depends on the kind of predictive problem you're trying to solve (more about this later), but the basic principle is to try to *fit* the data to a function in which the values of the features can be used to calculate the label.
+3. The result of the algorithm is a *model* that encapsulates the calculation derived by the algorithm as a *function* - let's call it ***f***. In mathematical notation:
 
     ***y = f(x)***
 
-1. Now that the *training* phase is complete, the trained model can be used for *inferencing*. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you'll often see the output from the function shown as ***&#375;*** (which is rather delightfully verbalized as "y-hat").
+4. Now that the *training* phase is complete, the trained model can be used for *inferencing*. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you'll often see the output from the function shown as ***&#375;*** (which is rather delightfully verbalized as "y-hat").
 
 ::: zone-end
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/3-types-of-machine-learning.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/3-types-of-machine-learning.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=94a8b36d-6d42-486b-99fa-b151b3a0ff88]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=5ed32be2-415e-4fd1-b3e3-2fb29d85d131]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/4-regression.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/4-regression.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=ead5dd72-0a6b-41f9-a4d6-147a093d105a]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=7e8451fb-ac05-4979-8cf8-e65d3767eac3]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
@@ -16,9 +16,9 @@ Regression models are trained to predict numeric label values based on training
 The diagram shows four key elements of the training process for supervised machine learning models:
 
 1. Split the training data (randomly) to create a dataset with which to train the model while holding back a subset of the data that you'll use to validate the trained model.
-1. Use an algorithm to fit the training data to a model. In the case of a regression model, use a regression algorithm such as *linear regression*.
-1. Use the validation data you held back to test the model by predicting labels for the features.
-1. Compare the known *actual* labels in the validation dataset to the labels that the model predicted. Then aggregate the differences between the *predicted* and *actual* label values to calculate a metric that indicates how accurately the model predicted for the validation data.
+2. Use an algorithm to fit the training data to a model. In the case of a regression model, use a regression algorithm such as *linear regression*.
+3. Use the validation data you held back to test the model by predicting labels for the features.
+4. Compare the known *actual* labels in the validation dataset to the labels that the model predicted. Then aggregate the differences between the *predicted* and *actual* label values to calculate a metric that indicates how accurately the model predicted for the validation data.
 
 After each train, validate, and evaluate iteration, you can repeat the process with different algorithms and parameters until an acceptable evaluation metric is achieved.
 
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/5-binary-classification.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/5-binary-classification.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=1c37e9d1-ff08-4fc7-9ce1-4a950061d008]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=ab342d0c-cc50-4d43-957b-f58042009fa6]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/6-multiclass-classification.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/6-multiclass-classification.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=3bb4daf5-6b34-45d7-9c53-ede8ed6ea6db]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=3a9d28c2-cc84-4447-ab3e-d0d6a86b9621]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/7-clustering.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/7-clustering.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=b485720c-8027-4124-920c-323597115a8d]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=bd45cea3-e10d-4aba-ae8d-db759299a446]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/8-deep-learning.md b/learn-pr/wwl-data-ai/fundamentals-machine-learning/includes/8-deep-learning.md
@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=4d3c9451-7ece-40a8-a755-6862bc9961f7]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=963e282f-5f8a-4375-ac40-10ead2bc2d5e]
 
 > [!NOTE]
 > See the **Text and images** tab for more details!
diff --git a/learn-pr/wwl-data-ai/fundamentals-machine-learning/index.yml b/learn-pr/wwl-data-ai/fundamentals-machine-learning/index.yml
@@ -3,7 +3,7 @@ uid: learn.wwl.fundamentals-machine-learning
 metadata:
   title: Introduction to Machine Learning Concepts
   description: "Machine learning is the basis for most modern artificial intelligence solutions. A familiarity with the core concepts on which machine learning is based is an important foundation for understanding AI."
-  ms.date: 09/10/2025
+  ms.date: 03/27/2026
   ms.update-cycle: 180-days
   author: wwlpublish
   ms.author: gmalc
