AI-900: Microsoft Azure AI Fundamentals

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Summary

  • beginner
  • azure
  • azure-bot-service
  • azure-cognitive-search
  • azure-cognitive-services
  • azure-computer-vision
  • azure-form-recognizer
  • azure-language-understanding
  • azure-machine-learning
  • azure-qna-maker
  • azure-speech-text
  • azure-speech-translation
  • azure-text-analytics
  • azure-text-speech
  • azure-translator-speech
  • azure-translator-text
  • The content of this exam was updated on January 27, 2021. Please download the skills measured document below to see what changed.

    Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

    This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

    This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

    Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Learning paths

3 hr 18 min
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Learn how to use Azure Machine Learning to create and publish models without writing code.

Modules in this learning path

  • Use automated machine learning in Azure Machine Learning
    9 Units
    39 min

    Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.

  • Create a Regression Model with Azure Machine Learning designer
    10 Units
    51 min

    Regression is a supervised machine learning technique used to predict numeric values. Learn how to create regression models using Azure Machine Learning designer.

  • Create a classification model with Azure Machine Learning designer
    10 Units
    55 min

    Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.

  • Create a Clustering Model with Azure Machine Learning designer
    10 Units
    53 min

    Clustering is an unsupervised machine learning technique used to group similar entities based on their features. Learn how to create clustering models using Azure Machine Learning designer.

27 min
Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you get started.

Modules in this learning path

  • Get started with AI on Azure
    10 Units
    27 min

    With AI, we can build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

2 hr 6 min
Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural language.

Modules in this learning path

  • Analyze text with the Text Analytics service
    5 Units
    29 min

    The Text Analytics service is a cloud-based service that provides advanced natural language processing over raw text for sentiment analysis, key phrase extraction, named entity recognition, and language detection.

  • Recognize and synthesize speech
    5 Units
    29 min

    Learn how to recognize and synthesize speech by using Azure Cognitive Services.

  • Translate text and speech
    5 Units
    29 min

    Automated translation capabilities in an AI solution enables closer collaboration by removing language barriers.

  • Create a language model with LUIS
    5 Units
    39 min

    In this module, we’ll introduce you to Language Understanding Intelligent Service (LUIS) and show how to create a LUIS application.

2 hr 53 min
Computer vision is an area of artificial intelligence (AI) in which software systems are designed to perceive the world visually, though cameras, images, and video. There are multiple specific types of computer vision problem that AI engineers and data scientists can solve using a mix of custom machine learning models and platform-as-a-service (PaaS) solutions - including many cognitive services in Microsoft Azure.

Modules in this learning path

  • Analyze images with the Computer Vision service
    5 Units
    28 min

    The Computer Vision service enables software engineers to create intelligent solutions that extract information from images; a common task in many artificial intelligence (AI) scenarios.

  • Classify images with the Custom Vision service
    5 Units
    34 min

    Image classification is a common workload in artificial intelligence (AI) applications. It harnesses the predictive power of machine learning to enable AI systems to identify real-world items based on images.

  • Detect objects in images with the Custom Vision service
    5 Units
    39 min

    Object detection is a form of computer vision in which artificial intelligence (AI) agents can identify and locate specific types of object in an image or camera feed.

  • Detect and analyze faces with the Face service
    5 Units
    24 min

    Face detection, analysis, and recognition is an important capability for artificial intelligence (AI) solutions. The Face cognitive service in Azure makes it easy integrate these capabilities into your applications.

  • Read text with the Computer Vision service
    5 Units
    24 min

    Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text.

  • Analyze receipts with the Form Recognizer service
    5 Units
    24 min

    Processing invoices and receipts is a common task in many business scenarios. Increasingly, organizations are turning to artificial intelligence (AI) to automate data extraction from scanned receipts.

1 hr 56 min
Conversational AI revolves around user interactions, in this case through Bots. In this learning path, you will learn how to create chat bots and integrate the bot with various AI services to add intelligence to the interaction. Learn how to integrate the QnA Maker Service (an easy method to add question and answers for bots), and explore the new Virtual Assistant scenarios for creating branded experiences with bots.