AI-900: Microsoft Azure AI Fundamentals Certification Video Training Course
The complete solution to prepare for for your exam with AI-900: Microsoft Azure AI Fundamentals certification video training course. The AI-900: Microsoft Azure AI Fundamentals certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including Microsoft Azure AI AI-900 exam dumps, study guide & practice test questions and answers.
AI-900: Microsoft Azure AI Fundamentals Certification Video Training Course Exam Curriculum
Introduction and basics on Azure
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1. Introduction to Azure5:00
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2. The Azure Free Account5:00
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3. Concepts in Azure4:00
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4. Quick view of the Azure portal4:00
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5. Lab - An example of creating a resource in Azure11:00
Describe AI workloads and considerations
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1. Machine Learning and Artificial Intelligence2:00
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2. Prediction and Forecasting workloads1:00
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3. Anomaly Detection Workloads1:00
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4. Natural Language Processing Workloads2:00
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5. Computer Vision Workloads1:00
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6. Conversational AI Workloads1:00
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7. Microsoft Guiding principles for response AI - Accountability2:00
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8. Microsoft Guiding principles for response AI - Reliability and Safety1:00
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9. Microsoft Guiding principles for response AI - Privacy and Security1:00
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10. Microsoft Guiding principles for response AI - Transparency1:00
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11. Microsoft Guiding principles for response AI - Inclusiveness1:00
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12. Microsoft Guiding principles for response AI - Fairness1:00
Describe fundamental principles of machine learning on Azure
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1. Section Introduction1:00
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2. Why even consider Machine Learning?4:00
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3. The Machine Learning Model9:00
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4. The Machine Learning Algorithms9:00
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5. Different Machine Learning Algorithms3:00
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6. Machine Learning Techniques4:00
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7. Machine Learning Data - Features and Labels5:00
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8. Lab - Azure Machine Learning - Creating a workspace6:00
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9. Lab - Building a Classification Machine Learning Pipeline - Your Dataset11:00
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10. Lab - Building a Classification Machine Learning Pipeline - Splitting data7:00
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11. Optional - Lab - Creating an Azure Virtual Machine9:00
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12. Lab - Building a Classification Machine Learning Pipeline - Compute Target6:00
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13. Lab - Building a Classification Machine Learning Pipeline - Completion6:00
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14. Lab - Building a Classification Machine Learning Pipeline - Results8:00
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15. Recap on what's been done so far2:00
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16. Lab - Building a Classification Machine Learning Pipeline - Deployment7:00
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17. Lab - Installing the POSTMAN tool4:00
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18. Lab - Building a Classification Machine Learning Pipeline - Testing6:00
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19. Lab - Building a Regression Machine Learning Pipeline - Cleaning Data9:00
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20. Lab - Building a Regression Machine Learning Pipeline - Complete Pipeline3:00
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21. Lab - Building a Regression Machine Learning Pipeline - Results3:00
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22. Feature Engineering3:00
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23. Automated Machine Learning6:00
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24. Deleting your resources2:00
Describe features of computer vision workloads on Azure
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1. Section Introduction2:00
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2. Azure Cognitive Services1:00
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3. Introduction to Azure Computer Vision solutions3:00
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4. A look at the Computer Vision service5:00
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5. Lab - Setting up Visual Studio 20194:00
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6. Lab - Computer Vision - Basic Object Detection - Visual Studio 201912:00
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7. Lab - Computer Vision - Restrictions example2:00
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8. Lab - Computer Vision - Object Bounding Coordinates - Visual Studio 20193:00
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9. Lab - Computer Vision - Brand Image - Visual Studio 20192:00
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10. Lab - Computer Vision - Via the POSTMAN tool5:00
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11. The benefits of the Cognitive services2:00
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12. Another example on Computer Vision - Bounding Coordinates2:00
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13. Lab - Computer Vision - Optical Character Recognition5:00
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14. Face API2:00
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15. Lab - Computer Vision - Analyzing a Face3:00
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16. A quick look at the Face service3:00
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17. Lab - Face API - Using Visual Studio 20196:00
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18. Lab - Face API - Using POSTMAN tool5:00
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19. Lab - Face Verify API - Using POSTMAN tool7:00
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20. Lab - Face Find Similar API - Using POSTMAN tool8:00
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21. Lab - Custom Vision9:00
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22. A quick look at the Form Recognizer service2:00
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23. Lab - Form Recognizer8:00
Describe features of Natural Language Processing and Conversational AI workloads
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1. Section Introduction1:00
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2. Natural Language Processing3:00
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3. A quick look at the Text Analytics1:00
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4. Lab - Text Analytics API - Key phrases4:00
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5. Lab - Text Analytics API - Language Detection1:00
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6. Lab - Text Analytics Service - Sentiment Analysis1:00
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7. Lab - Text Analytics Service - Entity Recognition3:00
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8. Lab - Translator Service3:00
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9. A quick look at the Speech Service1:00
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10. Lab - Speech Service - Speech to text4:00
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11. Lab - Speech Service - Text to speech1:00
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12. Language Understanding Intelligence Service2:00
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13. Lab - Working with LUIS - Using pre-built domains8:00
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14. Lab - Working with LUIS - Adding our own intents4:00
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15. Lab - Working with LUIS - Adding Entities2:00
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16. Lab - Working with LUIS - Publishing your model2:00
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17. QnA Maker service2:00
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18. Lab - QnA Maker service9:00
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19. Bot Framework2:00
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20. Example of Bot Framework in Azure3:00
Exam Practice Section
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1. About the exam5:00
About AI-900: Microsoft Azure AI Fundamentals Certification Video Training Course
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Describe features of computer vision workloads on Azure
1. Section Introduction
Welcome to this section. In this section, we are going to look at the Computer Vision API. The Computer Vision API now includes a plethora of features. The main focus that we are going to be looking at is the submission of images to the Computer Vision API and seeing what information this API can get back to us. So, for example, we can go ahead and submit an image to the service. It can actually go ahead and process the image. It can go ahead and tell us what objects are present in the image. So don't worry; we'll actually go through the service in this particular section. Now, when going through these services, when it comes to computer vision, when it comes to the cognitive suite of services, we are going to be using, in some cases, a Net program. Now, for the exam, you don't need to have any sort of development experience; you just need to showcase what the service can do. I want to show you examples of Net in Visual Studio 2019.
And again, it's going to be pretty simple. I'm not going to go ahead and create complicated programmes in the Net language. At the same time, I'm going to go ahead and also show you a very simple way to invoke these services, to invoke the Computer Vision service, and to invoke other services. In Azure, we're going to be using the Postman tool. So you don't need to have any development experience for that. I'll show you very clearly how you can actually go ahead and use these different services in Azure Cognitive Services. So we are going to look at the Computer Vision API. We will look at another interesting API that's known as the Face API. We will also look at the Customer Vision API. and also look at the Form Recognizer API. So all of these are the AzureCongress services that are available in Zah.
2. Azure Cognitive Services
Hi, welcome back. Now, when it comes to using the AI-based solutions that are available on Azure, we are going to be looking at Azure Cognitive Services. So there are a lot of services that we're going to go ahead and cover as part of this course. So some of the important services that we are going to add to COVID are, firstly, when it comes to the Vision API, so we are going to look at the Computer Vision API. We're going to be looking at custom vision. We're going to be looking at the Face API and the form recognizer. Aside from this, we also have the Speech API in place. So, if you want to convert speech to text, or if you want to do speech translation, all of this is available as part of this suite of services or APIs that are available as part of your cognitive services. So there are a lot of services that are available, and we're going to go in and cover the important ones, which are basically important from an exam perspective. So let's go ahead.
3. Introduction to Azure Computer Vision solutions
Hi, and welcome back. Now, in this chapter, I want to go through the different types of computer vision solutions that are available in Azure as part of Azure Cognitive Services. So first, we have the ability to go ahead and classify images. So we have something known as image classification. So over here, this involves analysing an image and identifying a class that the image falls under. So, for example, if you have a picture of a cat, as shown over here, So we, as human beings, know that this is a picture of a cat. But how does a computer know that when we submit this image, it is, in fact, a cat? So normally, you would need to go ahead and create machine learning models, train that model to go ahead and understand images of cats, and then go ahead and give us the solution or the answer that when we submit an image, this is indeed a cat.
However, when it comes to Azure and computer vision solutions, these machine learning models with image classification capabilities are already in place. They've already been trained. So we can actually go ahead and submit images to the computer vision service and have the ability to go out and classify images. It can also go ahead and do object detection as well. So over here, it has the ability to also go out and identify the different objects within the image itself. So, for example, you can go ahead and identify that there is a cat; it can identify that there are flowers. You can also get boundaries for the object itself. So, for example, this is very important when it comes to driving. So, when it comes to detecting cars on the road, it's important to go ahead and identify the car as an object and what the car's position is at that point in time if you want to go ahead and see the distance between cars.
So you can also go ahead and get the boundaries of the object as well. Apart from that, you also have optical character recognition. So over here, if you go ahead and submit an image that has text in it, it has the ability to go ahead and scan the text from the image itself. It has another popular service known as facial recognition. So over here, you can get different aspects of the face itself. So you can tell whether the person is male or female over here. Just from the picture itself, you can get an idea of the person's estimated age. You'll be able to recognise a person as well. So again, these are all different features. when it comes to the computer vision solutions that are available in Azure.
4. A look at the Computer Vision service
Now, if you want to go ahead and see how, say, the Computer Vision Service works when it comes to Azure Cognitive Services, So you can go to the Azure Cognitive Services Homepage, and then scroll down to see the vision services that are available. Over here, you can actually go to the Computer Vision service. So this service is used to analyse your images and provide you with relevant data based on those images. So if you go ahead and scroll down, over here, there will be one default image in place, and over here, you're getting information about the image. Now over here, you also have the ability to go ahead and upload your own image.
So let me go ahead and do that. Let me go ahead and click on Browse and browse for an image that I have on my local machine. So I'm browsing for an image that I have on my local machine. Let me go ahead and hit Open. So now this particular service has actually gone ahead. It's taken my image, and it has also gone ahead and detected certain objects in the image. Over here, it has not gone ahead and detected all of the objects, but most of the objects have been detected via the service. Now for each of the objects that it has gone ahead and detected, it is going ahead and giving you relevant information over here about all the objects in that particular image, basically those that it has detected. So over here, you can see the object itself; it's a cup. Over here, you can see the confidence level.
So on a scale of zero to one, it's going in and giving you a confidence level based on how confident it is when it is making the decision on what the object is that it has gone ahead and detected. Now over here, you can also see bounding rectangular coordinates. So what this means is that it is going ahead and giving you coordinates about the underlying object that it has gone ahead and detected. So over here, when you're looking at, let's say, this bounding box for this particular object, it is going in and giving you the positional coordinates of the object itself, and then it also has the ability to go ahead and give you various tags. So over here, it has gone ahead and understood that, yes, there is a table. It also has a flow. So it has gone ahead and detected certain attributes about the object itself. So there's a lot of information that you can actually get via the service. You just go ahead and upload an image to the service. And over here, it's giving you these different attributes. So this is the power that you have with the computer vision service.
Now, please note that when it comes to these services in Azure, I'm going to go ahead and show you how you can call these services, for example, from an anet programme in Visual Studio 2019, or even from a tool that's a Postman tool where you can actually go ahead and call these APIs. Now, the entire reason I'm actually going ahead and showcasing these examples is because I always want students to have a good understanding of the services that are present in Azure and how you can make use of them. Have you looked over here on this page? So it's giving you a description and an example of what this service can do in your organization, in your company.
So you're not going to use a space to go ahead and basically tell me attributes about an image or an object? No, you will be developing applications or being responsible for the implementation of applications that will actually go ahead and use these services. So it's very important for you as an IT professional to go ahead and understand how you can invoke these services. And that's the extra level I actually give to my students. I want y'all to know the full power and capabilities of these services. So that's why I'm going to go ahead and give it extra when it comes to how you work with your cognitive services.
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