AI900 Azure AI Fundamental Complete Course + Quiz Questions

Identify patterns in data to predict or decide, building models that generalize beyond training data. Cover supervised learning with regression and classification, unsupervised clustering, and reinforcement learning.

Explore core machine learning algorithms and their roles in building models, from linear and logistic regression to decision trees, random forests, support vector machines, KNN, K-means clustering, and neural networks.

A model is a function learned from data via an algorithm to predict outcomes. Train with data, split into training vs testing sets, and evaluate accuracy on unseen data.

Build, train, deploy, and manage ML models in Azure Machine Learning Studio, a cloud-based platform with automated ML, data prep, experiments, evaluation, and responsible AI insights.

Learn to build a regression model for predicting ice cream sales with Azure Machine Learning Studio, from workspace setup to automated ML training, deployment, and testing endpoints.

Explore how generative ai creates original content with large language models and transformers, and compare it to traditional ml. Learn practical implications for developers, tools, and responsible ai.

Explore GenAI model types and APIs—from GPT, Llama, Claude, and Gemini to image, audio, and video generation—and how tools talk to models for automation.

Trace tokenization, embedding, and positional encoding, then dive into encoder and decoder blocks and self-attention. See how multi-head attention and the feed-forward network enable parallel processing and semantic representations.

Define AI agents as autonomous systems that perceive their environment, reason, and act using tools and memory to achieve goals, guided by plan-and-execute cycles and learning from feedback.

Explore Azure generative AI with AI Foundry, a pro-code platform to explore, test, and deploy foundation models, agents, and services with governance and responsible AI.

Explore Microsoft Foundry, the Azure AI Foundry platform for creating resources and projects and deploying models. Deploy models like GPT-4.1 and Claude variants, and build agents with tools and guardrails.

Learn the natural language processing pipeline from raw text to model training, covering text analysis, opinion mining, translation, summarization, and conversational AI, with statistical and semantic approaches and speech processing.

Explore azure ai language service and translator to analyze text with language detection, keyphrase extraction, named entities, sentiment, pii detection, summarization, and Q&A, plus document translation and enterprise-grade capabilities.

Explore how Microsoft Foundry enables Azure AI language service via the language playground, delivering sentiment analysis, key phrase extraction, named entity recognition, translation, and custom question answering for NLP tasks.

CNNs use kernels to extract hierarchical image features, convert pixels into a feature vector, and enable classification, detection, and captioning with multimodal foundation models.

Explore information extraction in Azure AI, covering four steps—source identification, extraction, transformation and structuring, and storage—using OCR, natural language processing, and document intelligence to digitize and structure multimodal data.

Explore Azure vision service for image and video analysis, optical character recognition, and object tagging, and learn face detection and recognition with blurriness, head pose, and occlusion checks.

Explore AI powered information extraction in azure, comparing azure ai vision, azure ai document intelligence, and azure ai content understanding for text, forms, images, audio, and video.