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

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.

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.

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

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