Implement a Data Science and Machine Learning Solution for AI with Microsoft (DP-604)

Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.

Description

 

Students will learn

  • 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

Course Outline

 

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

 

 

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