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learn-pr/azure-fundamentals/intro-to-azure-fundamentals/includes/5-get-started-with-azure-accounts.md

@@ -1,5 +1,12 @@
 To create and use Azure services, you need an Azure subscription. When you're completing Learn modules, most of the time a temporary subscription is created for you, which runs in an environment called the Learn sandbox. When you're working with your own applications and business needs, you need to create an Azure account, and a subscription will be created for you. After you've created an Azure account, you're free to create additional subscriptions. For example, your company might use a single Azure account for your business and separate subscriptions for development, marketing, and sales departments. After you've created an Azure subscription, you can start creating Azure resources within each subscription.
 
+The following explains how Azure separates identity and billing information using accounts and subscriptions.
+
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=67fd032f-58ba-4dd9-8ae1-625b48e0bc20]
+
+> [!NOTE]
+> This video was created with the help of artificial intelligence. To learn more about our principles for AI-generated content, visit [AI Content Principles](/principles-for-ai-generated-content).
+
 :::image type="content" source="../media/scope-levels-12669ee1.png" alt-text="Illustration showing the different levels of account scope.":::
 
 

learn-pr/azure/create-an-azure-account/includes/4-multiple-subscriptions.md

@@ -2,6 +2,14 @@ With Azure, you only pay for what you use. You'll receive a monthly invoice with
 
 ## Azure subscription
 
+The following explains how Azure separates identity and billing information using accounts and subscriptions.
+
+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=67fd032f-58ba-4dd9-8ae1-625b48e0bc20]
+
+> [!NOTE]
+> This video was created with the help of artificial intelligence. To learn more about our principles for AI-generated content, visit [AI Content Principles](/principles-for-ai-generated-content).
+
+
 When you sign up, an Azure subscription is created by default. An Azure subscription is a logical container used to provision resources in Azure. It holds the details of all your resources like virtual machines (VMs), databases, and more. When  you create an Azure resource like a VM, you identify the subscription it belongs to.  As you use the VM, the usage of the VM is aggregated and billed monthly.
 
 ## Create additional Azure subscriptions

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

@@ -2,16 +2,18 @@ Not all models are simple mathematical equations that can be plotted as a line.
 
 ## 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 see how it can be tackled 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 would 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 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
 
 * Familiarity with machine learning models
 
 ## Learning objectives
 
-* Discover new model types– decision trees and random forests.
-* Learn how model architecture can affect performance
-* Practice working with hyperparameters to improve training effectiveness
+In this module, you will:
+
+* Discover new model types: decision trees and random forests.
+* Learn how model architecture can affect performance.
+* Practice working with hyperparameters to improve training effectiveness.

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

@@ -1,20 +1,20 @@
-When we talk of architecture, we often think of buildings. Architecture is responsible for how a building is structured – its height, depth, the number of floors, and how things are connected internally. This architecture also dictates how we use a building – where we enter it, and what we can “get out of it”, practically speaking.
+When we talk about architecture, we often think of buildings. Architecture is responsible for how a building is structured; its height, its depth, the number of floors, and how things are connected internally. This architecture also dictates how we use a building: where we enter it and what we can "get out of it," practically speaking.
 
-In machine learning, architecture refers to a similar concept. How many parameters does it have, and how are they linked together to achieve a calculation? Do we calculate a lot in parallel (width) or do we've serial operations that rely on a previous calculation (depth)? How can we provide inputs to this model, and how can we receive outputs? Such architectural decisions only typically apply to more complex models, and architectural decisions can range from simple to complex. These decisions are usually made before the model is trained, though in some circumstances there's room to make changes post-training.
+In machine learning, architecture refers to a similar concept. How many parameters does it have, and how are they linked together to achieve a calculation? Do we calculate a lot in parallel (width) or do we have serial operations that rely on a previous calculation (depth)? How can we provide inputs to this model, and how can we receive outputs? Such architectural decisions only typically apply to more complex models, and architectural decisions can range from simple to complex. These decisions are usually made before the model is trained, though in some circumstances there's room to make changes post-training.
 
 Let’s explore this more concretely with decision trees as an example.
 
 ## What's a decision tree?
 
 In essence, a decision tree is a flow chart. Decision trees are a categorization model that breaks down decisions into multiple steps.
 
-![Diagram showing a decision tree of gender, age and survival rate.](../media/7-2-a.jpg)
+![Diagram showing a decision tree of gender, age, and survival rate.](../media/7-2-a.jpg)
 
-The sample if provided at the entry point (top, in the diagram above) and each exit point has a label (bottom in the diagram). At each node, a simple ‘if’ statement decides which branch the sample passes to next. Once the branch has reached the end of the tree (the leaves), it will be assigned to a label.
+The sample if provided at the entry point (top, in the diagram above) and each exit point has a label (bottom in the diagram). At each node, a simple "if" statement decides which branch the sample passes to next. Once the branch has reached the end of the tree (the leaves), it will be assigned to a label.
 
 ### How are decision trees trained?
 
-Decision trees are trained one node, or decision point, at a time. At the first node, the entire training-set is assessed. From there a feature is selected that can best separate the set into two subsets that have more homogenous labels. For example, imagine our training set was as follows:
+Decision trees are trained one node, or decision point, at a time. At the first node, the entire training-set is assessed. From there, a feature is selected that can best separate the set into two subsets that have more homogenous labels. For example, imagine our training set was as follows:
 
 | Weight (Feature)  | Age (Feature) | Won a medal (Label) |
 |----------------------|-----------------|------------|
@@ -27,7 +27,7 @@ Decision trees are trained one node, or decision point, at a time. At the first
 | 85                   | 26              | Yes        |
 | 90                   | 25              | Yes        |
 
-If we're to do our best to find a simple rule to split this data, we might split by age, at around 24 years old, because most medal winners were over 24. This split would give us two subsets of data.
+If we're doing our best to find a rule to split this data, we might split by age at around 24 years old, because most medal winners were over 24. This split would give us two subsets of data.
 
 **Subset 1**
 
@@ -47,24 +47,24 @@ If we're to do our best to find a simple rule to split this data, we might split
 | 85                   | 26              | Yes        |
 | 90                   | 25              | Yes        |
 
-If we stop here, we've a simple model with one node and two leaves. Leaf 1 contains non-medal winners, and is 75% accurate on our training set. Leaf 2 contains medal winners, and is also 75% accurate on the training set.
+If we stop here, we have a simple model with one node and two leaves. Leaf 1 contains non-medal winners, and is 75% accurate on our training set. Leaf 2 contains medal winners, and is also 75% accurate on the training set.
 
 We don’t need to stop here, though. We can continue this process by splitting the leaves further.
 
-In subset 1, the first new node could split by weight, because the only medal winner had a weight less than people who didn't win a medal. The rule might be set to “weight < 65”. People with weight < 65 are predicted to have won a medal. While anyone with weight ≥65 don't meet this criterion, and might be predicted to not win a medal.
+In subset 1, the first new node could split by weight, because the only medal winner had a weight less than people who didn't win a medal. The rule might be set to "weight < 65". People with weight < 65 are predicted to have won a medal, while anyone with weight ≥65 don't meet this criterion, and might be predicted to not win a medal.
 
-In subset 2, the second new node might also split by weight, but this time predicts that anyone with a weight over 70 would have won a medal, while those under it would not.
+In subset 2, the second new node might also split by weight, but this time predicts that anyone with a weight over 70 would have won a medal, while those under it wouldn't.
 
 This would provide us with a tree that could achieve 100% accuracy on the training set.
 
 ### Strengths and weaknesses of decision trees
 
 Decision trees are considered to have low bias. This means that they're usually good at identifying features that are important in order to label something correctly.
 
-The major weakness of decision trees is overfitting. Consider the example given above: the model gives an exact way to calculate who is likely to win a medal, and this will predict 100% of the training dataset correctly. This level of accuracy is unusual for machine learning models, which normally make numerous errors on training dataset. Good training performance isn't a bad thing in itself, but the tree has become so specialized to the training set that it probably won't do well on the test set. This is because the tree has managed to learn relationships in the training set that probably aren't real – such as that having a weight of 60 kg guarantees a medal if you are under 25 years old.
+The major weakness of decision trees is overfitting. Consider the example given previously: the model gives an exact way to calculate who is likely to win a medal, and this will predict 100% of the training dataset correctly. This level of accuracy is unusual for machine learning models, which normally make numerous errors on training dataset. Good training performance isn't a bad thing in itself, but the tree has become so specialized to the training set that it probably won't do well on the test set. This is because the tree has managed to learn relationships in the training set that probably aren't real, such as that having a weight of 60 kg guarantees a medal if you're under 25 years old.
 
 ## Model architecture affects overfitting
 
-How we structure our decision tree is key to avoiding its weaknesses. The deeper the tree is, the more likely it's to overfit the training set. For example, in the simple tree above, if we limited the tree to only the first node, it would make errors on the training set, but probably do better on the test set. This is because it would have more general rules about who wins medal, such as “athletes over 24”, rather than extremely specific rules that might only apply to the training set.
+How we structure our decision tree is key to avoiding its weaknesses. The deeper the tree is, the more likely it is to overfit the training set. For example, in the simple tree above, if we limited the tree to only the first node, it would make errors on the training set, but probably do better on the test set. This is because it would have more general rules about who wins medals, such as "athletes over 24," rather than extremely specific rules that might only apply to the training set.
 
-Although we're focused on trees here, other complex models often have similar weakness that can be mitigated through decisions about how they're structured, or how they're allowed to be manipulated by the training.
+Although we're focused on trees here, other complex models often have similar weakness that we can mitigate through decisions about how they're structured or how they're allowed to be manipulated by the training.