Course Overview
Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.
What are the skills covered
- Get started with data science in Microsoft Fabric
- Explore data for data science with notebooks in Microsoft Fabric
- Preprocess data with Data Wrangler in Microsoft Fabric
- Train and track machine learning models with MLflow in Microsoft Fabric
- Generate batch predictions using a deployed model in Microsoft Fabric
Who should attend this course
- Data Scientist
- Data Analyst
- Data Engineer
Course Curriculum
What are the Prerequisites
- You should be familiar with basic data concepts and terminology.
Download Course Syllabus
Course Modules
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.
Learning objectives
In this module, you’ll learn how to:
- Understand the data science process
- Train models with notebooks in Microsoft Fabric
- Track model training metrics with MLflow and experiments
Prerequisites
Before starting this module, you should be familiar with the basic principles of machine learning.
Microsoft Fabric notebooks serve as a comprehensive tool for data exploration, enabling users to uncover hidden patterns and relationships in their datasets.
Learning objectives
In this module, you’ll:
- Load data and perform initial data exploration.
- Gain knowledge about different types of data distributions.
- Understand the concept of missing data, and strategies to handle missing data effectively.
- Visualize data using various data visualization techniques and libraries.
Prerequisites
Before starting this module, you should be familiar with the basic principles of machine learning.
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.
Learning objectives
In this module, you’ll:
- Learn Data Wrangler features, and its role in the data science workflow.
- Perform different types of preprocessing operations in data science.
- Learn how to handle missing values, and imputation strategies.
- Use one-hot encoding and other techniques to convert categorical data into a format suitable for machine learning algorithms.
Prerequisites
Before starting this module, you should be familiar with the basic principles of machine learning.
In Microsoft Fabric, data scientists can train models in notebooks, track their work in experiments, and manage their models with MLflow.
Learning objectives
In this module, you’ll learn how to:
- Train machine learning models with open-source frameworks
- Train models with notebooks in Microsoft Fabric
- Track model training metrics with MLflow and experiments in Microsoft Fabric
Prerequisites
Before starting this module, you should be familiar with the data science process.
Save and use your machine learning models in Microsoft Fabric to generate batch predictions and enrich your data.
Learning objectives
In this module, you’ll learn how to:
- Save a model in the Microsoft Fabric workspace
- Prepare a dataset for batch predictions
- Apply the model to dataset to generate new predictions
- Save the predictions to a Delta table
Prerequisites
Before starting this module, you should be familiar with the data science process.
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Training Options
- ILT: Instructor-Led Training
- VILT: Virtual Instructor-Led Training
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Exam & Certification
To earn this Microsoft Applied Skills credential, learners demonstrate the ability to implement a data science solution by using Microsoft Fabric, including:
- Ingesting, loading, exploring, and preparing data
- Training, tracking, and scoring a model
Candidates for this credential should be familiar with data science and AI fundamentals, in addition to open-source frameworks, such as scikit-learn and SynapseML. They should also have experience with:
- Python
- MLflow
- Synapse Data Science in Microsoft Fabric
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