Microsoft Fabric Machine Learning Fundamentals

What you’ll learn and how you can apply it

• Describe the data analytics lifecycle and how it’s implemented within the Microsoft Fabric environment
• Use Fabric lakehouses and notebooks to load, clean, and prepare data for machine learning
• Perform exploratory data analysis (EDA) in a Fabric notebook to identify trends and prepare features for modeling
• Understand and navigate key data science components in Microsoft Fabric
• Use both the graphical interface and code within Fabric to build and run a linear regression experiment
• Use runs to iteratively improve model performance, tracking and comparing different versions
• Score and generate predictions on new data within the Fabric environment
• Visualize and interpret model outputs using Power BI to support data-driven decision-making

Course description

Many data professionals struggle to turn prototypes into scalable, production-ready machine learning solutions. Microsoft Fabric is a unified data platform that enables end-to-end analytics across data engineering, data warehousing, and data science workflows. Data analytics expert Nicki Tinson shows you how Fabric’s integrated tools can streamline machine learning operations. Through a hands-on scenario, you’ll explore how to build, evaluate, and deploy ML workflows within Microsoft Fabric’s data science experience. Using linear regression, you’ll follow the complete data analytics lifecycle from sourcing and preparing data in a lakehouse, to exploring it using notebooks, to building a predictive model using both GUI- and code-based tools. You’ll learn how to create and manage experiments and runs to iteratively improve your models and how to score new data and visualize predictions in Power BI. You’ll leave knowing how to operationalize machine learning in Fabric and apply these techniques to real-world business problems in your own organization.