Module 1: Get started with data science in Microsoft Fabric
- Understand the data science process
- Train models with notebooks in Microsoft Fabric
- Track model training metrics with MLflow and experiments
Module 2: Explore data for data science with notebooks in Microsoft Fabric
- 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.
Module 3: Preprocess data with Data Wrangler in Microsoft Fabric
- 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.
Module 4: Train and track machine learning models with MLflow in Microsoft Fabric
- 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
Module 5: Generate batch predictions using a deployed model in Microsoft Fabric
- 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
