Anomaly detection can be a critical part of almost any business and can be used for fraud detection, identifying failures, and noticing unusual patterns in logs, records, or any time series based data. In this course, you'll learn the purpose and uses for anomaly detection and how AI anomaly detection can be used to identify unusual patterns, failures, and fraud. You'll then learn about the challenges of detecting anomalies in real world situations and how AI-based anomaly detection can be used to mitigate those challenges. Finally, you'll learn how to build, configure, deploy, and test the Azure Anomaly Detection service to create anomaly detection services you can use in real world scenarios. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Artificial Intelligence and machine learning in particular are solving a significant number of business and social problems and giving computers a new way to handle and process vast amounts of data. In this course, you'll learn about AI and machine learning concepts regarding regression, classification, and clustering algorithms. You'll explore how to manage datasets and work with labeled versus unlabeled data. You'll learn how supervised and unsupervised machine learning can be used, as well as how to build and use AIs safely, transparently, and fairly. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

The Azure Machine Learning Studio provides a proficient designer that can be used to build machine learning pipelines. In this course, you'll explore Azure ML Studio Designer and practice using its features for managing, normalizing, and transforming data for use in regression, classification, and clustering models. You'll also learn how to select features from a dataset and create datasets for training and validating models within Studio Designer. By the end of the course, you'll be able to use datasets, add transformations, normalize and clean data, split datasets, and configure and use a number of models, all within Azure ML Studio Designer. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Computer vision is the machine learning capability to allow computers to "see" similar to how a person can see, and be able to identify, distinguish, and interpret objects, people, and even text from images or video. In this course you will learn about the Azure ML Computer Vision service, Computer Vision Models, and how it can be trained and used to detect and classify objects in images and videos using a Classifier model and semantic segmentation. You'll also learn how to evaluate the results of an object detection model. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Conversational bots are becoming a powerful tool for businesses since they can interact and respond to queries and questions similar to how a real person would. However, they are not limited to this use and can also become personal digital assistants and be used as knowledgebases. In this course, you'll learn about the QnA Maker for knowledgebase bots, how to create a conversational bot, how to connect the bot to external channels and apps, and even how to create a simple personal digital assistant. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

In order to build a powerful and useful machine learning deployment, you must be able to evaluate and verify the AI model and data, as well as the accuracy and effectiveness of its predictions. Azure Machine Learning Studio and the Designer provide multiple easy-to-use methods for evaluating and scoring a model. In this course, you'll learn how to score and evaluate models and interpret and evaluate the results from some common models. You'll also explore how to create an inference pipeline, add web service output to provide external access to the model, and deploy and test a predictive web service. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

An advanced and powerful feature of computer vision is the ability to detect, identify, and even analyze faces and forms in images and videos. In this course, you'll learn how to use the Azure Face Detection service and Computer Vision service to determine a person's age from an image and to determine their sentiment. You'll also explore how to use the Form Recognizer service to read and process forms using Optical Character Recognition. Finally, you'll learn how to use the Receipt and Custom Form Recognizer to process fields from receipts. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Azure ML provides a suite of services to help with machine learning by providing a single interface to build, manage, deploy, test, and collaborate via the Azure Machine Learning Studio. In this course, you'll learn about the Azure ML services provided, including as Machine Learning designer and automated machine learning. You'll explore how to access and use the Azure Machine Learning Studio and review the Machine Learning features available in the service. In particular, you'll learn about the features of the Computer Vision, Custom Vision, Face, and Form Recognizer services. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

A powerful feature of machine learning is Natural Language Processing. Natural language processing allows computers to identify and process natural language and can be used for speech-to-text and text-to-speech processing, sentiment analysis, and translation. In this course, you'll learn about the features, uses, and challenges of NLP and the Azure services supporting NLP. You'll also explore intents and entities. In particular, you'll learn to train models using the Azure services for text analytics, speech-to-text, text-to-speech, sentiment analysis, and translation. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

The Azure Machine Learning Studio is a complete web tool and graphical user interface for building, managing, deploying, evaluating, and testing machine learning algorithms and workloads from initial design to final deployment. In this course, you'll investigate the different features of the Azure ML Studio interface and use it to create datasets, ingest data, create models automatically, build prediction services, and finally, manage endpoints for a machine learning model. Furthermore, you'll explore the datastores, compute resources, experiments, pipelines, and model management interfaces that are utilized when working with Azure ML Studio. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Disaster recovery is proactive planning. Discover how to back up Azure data and plan Azure site recovery.