Implement a Data Science and Machine Learning solution for AI with Microsoft Fabric

• 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

Students willing to Implement data science and machine learning for AI in Microsoft Fabric

You should be familiar with basic data concepts and terminology.