Microsoft AI courses provide comprehensive coverage of AI and machine learning concepts for all skill levels, providing hands-on experience with tools like Azure Machine Learning and Dynamics 365 Commerce. They emphasize practical applications, advanced techniques and responsible practices of AI, enabling learners to develop and deploy AI solutions ethically and effectively. This article lists the best Microsoft AI courses that provide essential skills to excel in the field of artificial intelligence.
Fundamentals of Machine Learning
This course provides a fundamental understanding of machine learning, including its basic concepts, types, and considerations for training and evaluating models. It also covers the fundamentals of deep learning and the use of automated machine learning in the Azure Machine Learning service.
Create machine learning models
This course is ideal for those with a background in machine learning or a strong math background, focusing on quick-learning tools such as scikit-learn, TensorFlow, and PyTorch. It provides just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.
Implement a data science and machine learning solution for AI in Microsoft Fabric
This course covers the data science process in Microsoft Fabric, teaching how to train machine learning models, preprocess data, and manage models with MLflow. It includes modules on exploring data with notebooks, using Data Wrangler for preprocessing, and generating batch predictions with deployed models.
Microsoft Azure AI Fundamentals
This course introduces AI fundamentals and Microsoft Azure services for AI solutions, with the goal of increasing awareness of relevant AI workloads and Azure services. It targets individuals with basic computer science and mathematics skills, covering AI workloads, computer vision, natural language processing, document intelligence, and generative AI through level modules beginner.
Build a RAG-based co-pilot solution with your own data using Azure AI Studio
This course covers using Retrieval Augmented Generation (RAG) to enhance language models with specific data, indexing data with Azure AI Search, and creating a co-pilot in Azure AI Studio. It aims to improve AI-based content suggestions and generation.
Use product recommendations in Dynamics 365 Commerce
This module covers enabling and using product recommendations in Dynamics 365 Commerce, which uses AI and machine learning to analyze purchasing trends and provide relevant recommendations. This includes learning recommendation lists and settings.
Fundamentals of Responsible Generative AI
This module teaches how to develop generative AI solutions responsibly by describing a process for minimizing harmful content. It covers the identification, measurement and mitigation of potential harm, as well as preparation for the responsible deployment and operation of generative AI solutions.
Apply rapid engineering with Azure OpenAI Service
This course teaches prompt engineering in Azure OpenAI, focusing on designing and optimizing prompts to improve model performance. It covers creating clear instructions, asking for specific output compositions, and using contextual content to improve the accuracy and relevance of responses.
Work with generative artificial intelligence (AI) models in Azure Machine Learning
This course explores the application of generative AI models for NLP in Azure Machine Learning, covering topics such as understanding Transformer architecture and using large language models (LLM). It includes modules on fine-tuning LLMs for specific tasks and using a prompt flow to develop applications leveraging LLMs, with prerequisites of familiarity with Azure and the Azure portal.
Responsible use of artificial intelligence in education
This course explores Microsoft's Responsible AI framework, emphasizing ethical principles of AI development and application such as fairness, trustworthiness, privacy, inclusiveness, transparency and the responsibility. It includes modules on understanding and applying these principles, particularly in learning environments, with interactive exercises for practical implementation.