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Azure Machine Learning and AI Exercises [Unofficial]

This repository contains 25+ full examples to start building (or level up) your Azure AI and Machine Learning skills.
Read the License file before embarking on your learning journey.

Getting Started

Prerequisites

To get started with Azure Machine Learning, you will need the following:

  1. Azure Account: A Microsoft Azure account is essential. If you don't have one, you can sign up for a free account which provides limited access to Azure services and a certain amount of free credits to explore services.
  2. Azure Subscription: Within your Azure account, you'll need an active subscription. The free account includes a free subscription with limited credits. For ongoing or more extensive projects, you may need to set up a paid subscription.
  3. Resource Group: Azure organizes resources into resource groups, which are containers that hold related resources for an Azure solution. You'll need to create or have a resource group where you can place your Azure Machine Learning resources.
  4. Azure Machine Learning Workspace: The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. You can create a workspace in the Azure portal, via the Azure CLI, or using the Azure Machine Learning SDK for Python.
  5. Computational Resources: Depending on your workload, you might need various computational resources. Azure Machine Learning supports a range of compute options, from your local machine to cloud-based compute instances, clusters, and inferencing resources.
  6. Knowledge of Python, C#, and .NET: The Azure Machine Learning SDK for Python can be used for most Azure machine learning tasks, from data preprocessing to model training and deployment. Familiarity with Python and data science libraries like NumPy, pandas, and sci-kit will be very helpful.
  7. Familiarity with Machine Learning Concepts: Understanding basic machine learning concepts and models is beneficial, as Azure Machine Learning is a platform for building, training, and deploying machine learning models.
  8. Azure CLI or Azure Machine Learning SDK for Python: For automation or advanced scenarios, familiarity with the Azure Command-Line Interface (CLI) or the Azure Machine Learning SDK for Python can be beneficial.
  9. IDE or Code Editor: A good integrated development environment (IDE) or code editor, such as Visual Studio Code, is helpful for writing and debugging your Python scripts or Jupyter notebooks.
  10. Azure Machine Learning Studio Access: For a no-code or low-code experience, you can use Azure Machine Learning Studio, a web-based interface for managing the machine learning lifecycle.

Overview

All images are the property of Microsoft Corporation.

Microsoft AI Portfolio

Microsoft AI Portfolio Overview

Microsoft AI/ML Services

Microsoft AI Portfolio Overview

Resources

  1. Azure Machine Learning Documentation: The official Azure Machine Learning documentation is a comprehensive resource that covers everything from basic concepts to advanced scenarios. You can start here: Azure Machine Learning documentation.
  2. Quickstarts and Tutorials: The documentation includes a variety of quickstarts and tutorials that are great for getting hands-on experience. Look for sections titled "Quickstarts" and "Tutorials" within the documentation.
  3. Microsoft Learn: Microsoft Learn offers interactive learning paths and modules that are specifically designed for Azure Machine Learning. These can be very helpful for beginners and cover both the theoretical aspects and practical exercises. Start with the Azure Machine Learning fundamentals on Microsoft Learn: Introduction to Azure Machine Learning.
  4. GitHub Repositories: Microsoft and the community often publish sample projects and exercises on GitHub. You can search for Azure Machine Learning repositories for sample code and projects. A good starting point is the Azure/MachineLearningNotebooks repository, which provides examples and best practices.
  5. Azure Machine Learning SDK for Python: If you're planning to use Python for Azure Machine Learning, the SDK documentation is invaluable. It includes API references, sample code, and guides: Azure Machine Learning SDK for Python.
  6. Community Forums and Support: Engaging with the Azure Machine Learning community can be very helpful. You can ask questions, share experiences, and get advice from other users on forums like Stack Overflow, the Microsoft Azure forums, or the MSDN forums.
  7. Azure Machine Learning Studio: For a more visual approach, explore the Azure Machine Learning Studio, which allows you to build, test, and deploy machine learning models using a drag-and-drop interface. It's a great way to get started without writing much code.