Microsoft’s AI courses offer robust education in AI and machine learning across a range of skill levels. By emphasizing practical usage, advanced techniques, and ethical AI practices, students learn how to develop and deploy AI solutions effectively and responsibly.
The “Fundamentals of machine learning” course provides a grounding in machine learning’s core concepts along with deep learning fundamentals. It also introduces automated machine learning within Azure Machine Learning service.
“Create machine learning models” primarily targets students with some machine-learning knowledge or strong mathematical backgrounds. Using fast learning tools like scikit-learn, TensorFlow, and PyTorch, students gain quick familiarity with machine learning applications like Azure ML and Azure Databricks.
“Implementing data science and machine learning solution for AI in Microsoft Fabric” course encompasses the process of data science in Microsoft Fabric, including training machine learning models, preprocessing data, and managing models with MLflow. Students learn to manipulate data with notebooks, employ Data Wrangler for preprocessing, and generate batch predictions with deployed models.
The “Microsoft Azure AI Fundamentals” course introduces AI basics and Microsoft Azure services for AI solutions, covering AI workloads, computer vision, natural language processing, document intelligence, and generative AI for beginners.
“Building a RAG-based copilot solution with your own data using Azure AI Studio” teaches students how to improve AI-driven suggestions and content generation using the Retrieval Augmented Generation (RAG).
“Working with product recommendations in Dynamics 365 Commerce” exposes students to AI and machine learning’s application in analyzing purchase trends to provide relevant product recommendations.
“Fundamentals of Responsible Generative AI” course teaches students how to build generative AI solutions responsibly by explaining a process for mitigating harmful content.
“Applying prompt engineering with Azure OpenAI Service” course focuses on leveraging prompt engineering to enhance model performance in Azure OpenAI.
“Working with generative artificial intelligence (AI) models in Azure Machine Learning” explores generative AI models’ application for NLP in Azure Machine Learning, including understanding the Transformer architecture and working with large language models (LLMs).
Lastly, the “Responsible use of artificial intelligence in education” course explores the responsible AI framework developed by Microsoft, particularly relevant principles such as fairness, reliability, privacy, inclusiveness, transparency, and accountability, especially in the context of learning environments. Students engage in interactive exercises to understand and apply these principles practically and effectively.
