AI-900: Microsoft Azure AI Fundamentals Certification

Explore common AI workloads in Azure, including prediction and demand forecasting, supervised learning, anomaly detection, computer vision, natural language processing, knowledge mining, and conversational AI.

Explore the six guiding principles for responsible AI—fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—and how they address unintended consequences in real-world AI deployments.

Apply fairness to AI by ensuring no bias in emergency triage or loan decisions. Prioritize reliability, safety, privacy, and security with testing for edge cases and real-world anomalies.

Explore the six AI principles, focusing on inclusiveness, transparency, and accountability to ensure fair, accessible technology and to explain high-impact decisions.

Navigate Microsoft's official AI principles with videos and detailed explanations on microsoft.com, and explore design guidelines and practical examples for human AI interaction on learn.microsoft.com.

Learn common machine learning types—regression, classification, and clustering—with supervised and unsupervised approaches on Azure, including numeric predictions, labeled outcomes, and data-driven insights from examples like recommendations and spam filters.

Define features and labels as the input variables the model uses, and understand the Goldilocks effect of selecting just the right features, illustrated with an insurance life expectancy example.

Split cleaned data into fair training and validation sets to build representative models in Azure ML. Evaluate regression and classification with mean squared error, accuracy, and precision.

Explain how confusion matrix and ROC curve relate to AUC, detailing true positives, false positives, true negatives, and false negatives, with AUC defined as ranking a positive above a negative.

Explore no-code machine learning with Azure Machine Learning and AutoML, which test algorithms on your data to find the best model, then use ML designer for data prep and deployment.

Create an Azure Machine Learning workspace with storage and key vault; select West US 2, launch studio, and configure compute and a cluster for automated ML and a no-code model.

Learn automated machine learning with a no-code model by creating a web-sourced data set, training a regression model on bike rentals, and deploying a web service on Azure.

Explore no-code machine learning with Azure ML designer, drag and visualize data, normalize features, exclude irrelevant columns or rows, select regression models, and evaluate predictions with practical examples.

Build a no-code machine learning pipeline with Azure ML designer to predict car prices using an automobile dataset, including data cleaning, normalization, training and testing, and a linear regression model.

Explore common computer vision workloads in Azure, including image classification, object detection with coordinates, semantic segmentation, OCR, and facial recognition, essential for the AI-900 exam.

Discover how Azure cognitive service groups computer vision features, including custom vision, face service, and form recognizer, with a single endpoint and key, and separate training and predictions.

Create an Azure cognitive service, retrieve its key and endpoint, install the Python package, and use a pre-built computer vision model to describe images and identify objects and faces.

learn how to use the custom vision service to create a project, upload and tag images, train the model, and test predictions with category probabilities.

Explore natural language processing workloads on Azure, including keyphrase extraction, entity recognition, sentiment analysis, and speech and translation services, plus considerations for conversational ai and bot integrations.

Explore Azure NLP capabilities, including text analytics for key phrases, entity detection, sentiment analysis, and language detection, plus Louis utterances, entities, and intents.

Learn about common conversational AI workloads, including web chatbots, voice-assisted assistants, and customer service bots, and how the bot framework and Q&A Maker enable natural, human-like responses.

Explore Azure conversational AI with core services: Q&A Maker for no-code chatbots and Azure Bot Service for enterprise-grade bots that build knowledge bases from FAQs and data scraped from websites.

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