Course Overview
This course is designed to build practical skills in implementing data science and machine learning solutions using Microsoft Fabric. The course explores the complete end-to-end data science process, from understanding and exploring data to preparing and transforming datasets for analysis. Students will learn how to train, evaluate, and track machine learning models, as well as how to deploy those models and generate predictions using Microsoft Fabric tools and capabilities.
Who should attend
This course is intended for data professionals and practitioners who regularly work with machine learning models and are responsible for building, evaluating, and deploying data science solutions. Students should already be familiar with the data science process, Python, and common open-source machine learning frameworks such as scikit-learn.
Prerequisites
You should be familiar with basic data concepts and terminology.
Course Content
Get started with data science in Microsoft Fabric
In Microsoft Fabric, data scientists can manage data, notebooks, experiments, and models while easily accessing data from across the organization and collaborating with their fellow data professionals.
Explore data for data science with notebooks in Microsoft Fabric
Microsoft Fabric notebooks serve as a comprehensive tool for data exploration, enabling users to uncover hidden patterns and relationships in their datasets.
Preprocess data with Data Wrangler in Microsoft Fabric
Data Wrangler serves as a comprehensive tool for preprocessing data. It enables users to clean data, handle missing values, and transform features to build machine learning models.
Train and track machine learning models with MLflow in Microsoft Fabric
In Microsoft Fabric, data scientists can train models in notebooks, track their work in experiments, and manage their models with MLflow.
Generate batch predictions using a deployed model in Microsoft Fabric
Save and use your machine learning models in Microsoft Fabric to generate batch predictions and enrich your data.
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