Microsoft Azure AI Fundamentals [AI-900]: Step By Step Activity Guides (Hands-On Labs)

This blog post covers Hands-On Labs that will help you understand topics that are covered in Microsoft Azure AI Fundamental (AI-900) Certification.

This post helps you with your self-paced learning as well as for your team learning. There are 5 Hands-On Labs in this course.

1. Create an Azure Machine Learning Workspace
2. Create a Classification Model using Azure ML designer
3. Image Classification with Custom Vision Service
4. Sentiment Analysis
5. Create, Train and Publish QnA maker service

Here’s the quick sneak-peak of how to start learning Artificial Intelligence and Machine Learning on Azure & to clear Microsoft Azure AI Fundamentals (AI-900).

Check More About Microsoft Certified Azure AI Fundamental (AI-900) Certification

Lab 1. Create An Azure Machine Learning Workspace

Azure ML Workspace is a centralized place where we deploy resources that are needed to create and publish an Azure Machine Learning Model. The workspace keeps a record of all training runs, including logs, metrics, output, and a snapshot of your scripts. This information is used to determine the best model that maps inputs to the outputs.

We can interact with Azure ML workspace through Azure ML studio, Azure ML SDK for Python or R, Azure ML CLI, and Azure ML VS code extension.

Note: Check what is Azure Machine Learning Studio 

Lab 2. Create A Classification Model Using Azure ML Designer

Microsoft Azure AI Fundamental Certification covers various Machine Learning models. The Classification model is a type of Supervised Machine Learning technique to classify the Data based on some training.  This guide covers how to create and deploy a classification model using Azure ML Designer with all the resources needed like Compute Targets, Dataset, Modules (normalization, two-class logistic regression, score, etc).

Lab 3. Image Classification Using Custom Vision Service

Custom Vision service uses a Machine Learning algorithm to analyze images. Custom Vision service can be divided into two features

• Image Classification: Providing one or more label (name) to an image.
• Object Detection: It gives coordinates of the images were the specific labeled image is found.

Difference between Computer vision & Custom Vision:

• Computer Vision: The Classification model is already trained on some previously provided images.
• Custom Vision: We submit images from our side to train the Classification model.

Note: What is Object Detection And Tracking In Azure Machine Learning?

Lab 4. Sentiment Analysis

Sentiment Analysis is a type of Natural Language Processing (NLP) in which based on keywords in the text the model will analyze the sentiment from the text. For example, We need to analyze various tweets & hashtags about COVID-19 on twitter that are positive or negative.

This guide covers how you create a sentiment analysis model using Azure ML service.

Lab 5. Create, Train & Publish QnA Maker Service

A solution that enables the conversation between humans and machines (bots) is called Conversational AI Solution. We can create a QnA service using the Azure Bot service. In this, we need to train the model with some question and answer pairs and after training the model we can ask random questions to the QnA service (Bot) created.

Note: The questions you ask the QnA Service need not be exactly the same but some keywords should match.

Related/References:

• [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
• [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
• Automated Machine Learning | Azure | Pros & Cons
• Object Detection and Tracking in Azure Machine Learning
• Azure Machine Learning Studio
• Azure Cognitive Services (Overview & Types)

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