[NEW] Microsoft Azure AI Fundamentals AI-900

Explore a concise overview of AI and its implications in the Microsoft Azure AI Fundamentals AI-900 framework.

Explore how machine learning uses data to find patterns, make predictions, and automate tasks, from labeling flavors to building and deploying models with Azure Machine Learning Service.

Explore how machine learning powers computer vision, from image classification and object detection with bounding boxes to semantic segmentation with masks and OCR, using Azure AI vision to analyze data.

Explore natural language processing with Azure AI Language and Azure Speech to analyze text, understand speech, translate phrases, and generate synthesized speech across languages.

Explore document intelligence and knowledge mining, extracting data from contracts, health records, and forms with ocr, then turning it into structured data to enable fast insights.

Explore generative AI and its challenges and risks, including Azure OpenAI models like GPT-4, Codex, and Dall-E, and how they enable content generation, semantic search, and language to code translation.

Explore responsible AI and the six guiding principles—fairness, accountability, inclusiveness, reliability and safety, privacy and security, and transparency—to ensure ethical, accountable, and transparent AI systems.

Explore how machine learning underpins modern AI solutions and serves as the foundation for understanding AI concepts and the process in this introductory chapter.

Explore the foundation of machine learning, where training defines a function to predict labels from features and inferencing applies the trained model to new data like ice cream sales.

Explore the tree of machine learning foundations, from supervised methods like regression and classification to unsupervised clustering, with emphasis on evaluation metrics, feature selection, and model iteration.

Explore deep learning through neural networks, training with backpropagation, activation and loss functions, and the role of GPUs, while using Azure ML to train, evaluate, and deploy models.

Learn to use automated machine learning in Azure Machine Learning to train, evaluate, and deploy a bike rentals regression model, then test a real-time endpoint.

Explore Azure AI services and their pre-built capabilities for language, image analysis, and content generation, enabling emotionally intelligent elder companion robots and wildlife conservation through image-based species identification.

Explore Azure AI services, a prebuilt suite for image recognition, natural language processing, speech, and text analytics, enabling AI driven search and text summarization in apps.

Link your Azure AI services resource to the content safety studio and use the REST API, SDKs, or Visual Studio to enable content moderation with secure authentication.

Explore Azure AI content safety studio to moderate text and image content with prebuilt models, test samples and severity scores from safe to high, and provision a content safety resource.

Explore Azure AI services, ready-to-use resources on the Azure platform, offering language, speech, vision, decision, search, and Azure Open AI capabilities.

Delve into computer vision in Azure as the second unit of the Microsoft Azure AI Fundamentals AI-900 course.

Explore concepts and techniques in computer vision, ai field that interprets images and videos by recognizing objects, scenes, and actions, and see how Microsoft Azure ai vision enables cloud-based applications.

Explore how computer vision treats images as numeric pixel arrays, from grayscale values to rgb channels, and how convolutional filtering highlights edges with Laplace filters.

Train convolutional neural networks to extract features with filters and predict image labels, then explore transformers and multimodal models like Florence that fuse image and text for classification and captioning.

Explore Azure AI Vision's pre-trained models on the Florence architecture, enabling OCR, image captioning, object detection, and tagging, with customizable data and Vision Studio labs.

Explore Azure Vision Studio to analyze store images, generate captions, dense captions, and extract tags, then perform object detection with bounding boxes and confidence thresholds.

Explore how computer vision analyzes image pixel values to categorize images, locate objects, label scenes, and read text, aided by Azure AI vision's ready-made image analysis toolkit.

Learn facial detection and analysis in artificial intelligence, using algorithms to locate and analyze faces in images or video for security, targeted advertising, missing person searches, and identity validation.

Learn how Azure AI Vision and the Azure Face Service detect faces with bounding boxes, analyze attributes, train models for recognition, and apply responsible AI with limited access policies.

Learn to detect faces in images using Azure Vision Studio and the Azure AI face service, including bounding box coordinates and mask-wearing attributes in sample images.

Learn azure optical character recognition with vision studio, extracting printed and handwritten text from images into pages, lines, and words with bounding boxes and read API results.

Experiment with optical character recognition in Azure AI Vision via Vision Studio to extract text from images, view bounding boxes, and study the hierarchical layout of regions, lines, and words.

Explore natural language processing, enabling computers to understand and respond to language. Use text analysis techniques to extract meaning from unstructured text, including sentiment analysis and text summarization.

Tokenize text into tokens, perform frequency analysis, and use tf-idf to reveal topics; apply logistic regression and embeddings for sentiment, semantic relationships and language tasks via Azure AI language service.

Apply Azure AI language text analysis to extract entities, detect language and PII, analyze sentiment, and identify key phrases from unstructured text across multiple languages, with resource configuration options.

Analyze hotel reviews using Azure AI language and Language Studio to extract key phrases and classify sentiment as positive or negative, showcasing NLP capabilities and text analysis.

Explore azure ai languages question answering to build conversational ai that quickly and accurately responds to customer queries across web chat, email, and social platforms.

Discover how Azure AI Language and Bot Service enable question answering and conversational bots, build a knowledge base from FAQs with Language Studio, and deploy via web chat and Teams.

Create a knowledge base of question and answer pairs from Margie's Travel FAQ in Language Studio, then deploy and test a bot with Azure Bot Service via web chat.

Explore the Turing test's link to natural conversations and AI understanding. See how Azure's language service powers voice commands, enterprise assistance, command and control, and end-to-end dialogues.

Define intents, entities, and utterances to train a conversational language understanding model, then use pre-built domains in Azure Language Studio to publish predictions to a resource.

Master conversational language understanding with language studio to create, train, and deploy a model that interprets intents and entities for home automation commands.

Explore how speech recognition and synthesis power voice controls for home and auto systems, answer spoken questions, and generate captions from audio, using pre-built and custom models.

Explore Azure AI Speech services for speech to text and text to speech, using acoustic and language models to transcribe live or file audio and synthesize speech with customizable voices.

Explore Azure AI speech studio by creating an AI speech resource and using real-time speech to text to transcribe audio, illustrating transcription workflows.

Explore document intelligence and knowledge mining in Microsoft Azure AI Fundamentals to learn how to extract insights from documents.

Unlock document intelligence to automate extraction, comprehension, and storage of data from receipts, identifying merchant details and totals, using OCR and Azure AI services with pre-built and custom models.

Document intelligence uses machine learning to extract text and data from documents by locating text layouts and bounding boxes, training models for each format to automate extraction.

Use Azure's pre-built receipt model to extract fields like date, total, merchant, and items. Train or deploy with the Document Intelligence Studio and connect apps via key and endpoint.

Discover how Azure ai search accelerates information retrieval and enables knowledge mining by indexing unstructured and structured data, using indexers and json serialization to populate a searchable index.

Learn how to provision an Azure AI search resource, create an index and indexer with the Import Data Wizard, and design queries using simple and Lucene syntax.

Explore the Azure AI search index UI to build a knowledge mining solution for Fourth Coffee, extracting data from blob storage and enriching it with AI skills.

Push through to the last section of the Microsoft Azure AI Fundamentals course and keep going through the generative AI material.

Explore how generative AI leverages language models to produce original content from natural language input and empower copilots for creative tasks.

Explore generative AI, large language models, and transformer architectures that generate text, images, and code from natural language prompts.

Explore how copilots, powered by generative ai, integrate into Microsoft 365, Dynamics 365, and Azure, enabling off-the-shelf, extended, or custom copilots to boost productivity and data insights.

Explore Microsoft Copilot in Edge to generate a business plan, logo, and investor-ready materials for a cleaning company, using prompts, OneDrive, and Outlook.

Azure AI Studio unifies Azure Machine Learning, Azure OpenAI, and other AI services into a single workspace for building and managing AI solutions. Deploy models, test prompts, and integrate data.

Explore responsible generative AI practices, identifying and prioritizing harms, measuring risks, and implementing layered mitigations across the model, safety, prompts, and user interfaces.

Operate a responsible generative AI solution by completing pre-release reviews for legal, privacy, and security compliance, block harmful outputs, and release to restricted group with incident response and rollback plans.

Celebrate completing the course and invite future students to leave a review, share your passing score on the Q&A or LinkedIn, and inspire others in AI.