Course Summary

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions.

Mastering the basics in AI can help you jump-start your career and get ready to dive deeper into the other technical opportunities Azure offers.

Earning your Azure AI Fundamentals certification can supply the foundation you need to build your career and demonstrate your knowledge of common AI and machine learning workloads – and what Azure services can solve for them. Validate your foundational knowledge of machine learning and AI concepts, along with related Azure services.

Module 1:Describe Artificial Intelligence workloads and considerations (15–20%)
• Identify features of common AI workloads
• Identify features of content moderation and personalization workloads
• Identify computer vision workloads
• Identify natural language processing workloads
• Identify knowledge mining workloads
• Identify document intelligence workloads
• Identify features of generative AI workloads
• Identify guiding principles for responsible AI
• Describe considerations for fairness in an AI solution
• Describe considerations for reliability and safety in an AI solution
• Describe considerations for privacy and security in an AI solution
• Describe considerations for inclusiveness in an AI solution
• Describe considerations for transparency in an AI solution
• Describe considerations for accountability in an AI solution

Module 2: Describe fundamental principles of machine learning on Azure (20–25%)
• Identify common machine learning techniques
• Identify regression machine learning scenarios
• Identify classification machine learning scenarios
• Identify clustering machine learning scenarios
• Identify features of deep learning techniques
• Describe core machine learning concepts
• Identify features and labels in a dataset for machine learning
• Describe how training and validation datasets are used in machine learning
• Describe Azure Machine Learning capabilities
• Describe capabilities of Automated machine learning
• Describe data and compute services for data science and machine learning
• Describe model management and deployment capabilities in Azure Machine Learning

Module 3: Describe features of computer vision workloads on Azure (15–20%)
• Identify common types of computer vision solution:
• Identify features of image classification solutions
• Identify features of object detection solutions
• Identify features of optical character recognition solutions
• Identify features of facial detection and facial analysis solutions
• Identify Azure tools and services for computer vision tasks
• Describe capabilities of the Azure AI Vision service
• Describe capabilities of the Azure AI Face detection service
• Describe capabilities of the Azure AI Video Indexer service

Module 4: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
• Identify features of common NLP Workload Scenarios
• Identify features and uses for key phrase extraction
• Identify features and uses for entity recognition
• Identify features and uses for sentiment analysis
• Identify features and uses for language modeling
• Identify features and uses for speech recognition and synthesis
• Identify features and uses for translation
• Identify Azure tools and services for NLP workloads
• Describe capabilities of the Azure AI Language service
• Describe capabilities of the Azure AI Speech service
• Describe capabilities of the Azure AI Translator service

Module 5: Describe features of generative AI workloads on Azure (15–20%)
• Identify features of generative AI solutions
• Identify features of generative AI models
• Identify common scenarios for generative AI
• Identify responsible AI considerations for generative AI
• Identify capabilities of Azure OpenAI Service
• Describe natural language generation capabilities of Azure OpenAI Service
• Describe code generation capabilities of Azure OpenAI Service
• Describe image generation capabilities of Azure OpenAI Service

There are no prerequisites to this course.

Describe Artificial Intelligence workloads and considerations (15–20%) Describe fundamental principles of machine learning on Azure (20–25%) Describe features of computer vision workloads on Azure (15–20%) Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%) Describe features of generative AI workloads on Azure (15–20%)

Following your booking, a confirmation message will be sent to all participants, ensuring you're well-informed of your successful enrollment. Calendar placeholders will also be dispatched to assist you in scheduling your commitments around the course. Rest assured, all course materials and access to necessary labs or platforms will be provided no later than one week before the course begins, allowing you ample time to prepare and engage fully with the learning experience ahead.

Our comprehensive training package includes all the necessary materials and resources to facilitate a full learning experience. Enrollees will be provided with detailed course content, encompassing a wide array of topics to ensure a thorough understanding of the subject matter. Additionally, participants will receive a certificate of completion to recognize their dedication and hard work. It's important to note that while the course fee covers all training materials and experiences, the examination fee for certification is not included but can be purchased separately.

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