In this one-day course students explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.
After completing this course, students will be able to:
Understand the data science process
Train models with notebooks in Microsoft Fabric
Track model training metrics with MLflow and experiments
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
Visualise data using various data visualisation techniques and libraries
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 strategie
Use one-hot encoding and other techniques to convert categorical data into a format suitable for machine learning algorithms.
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
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
As part of Lumify Group, Lumify Work has skilled more people in Microsoft technologies than any other organisation in Australia and New Zealand. We have a campus in the Philippines, too. We offer the broadest range of instructor-led training courses, from end user to architect level. We are proud to be the winner of the Microsoft MCT Superstars Award for FY24, which formally recognises us as having the highest quality Microsoft Certified Trainers in ANZ.
Data Scientists
Data Analysts
Data Engineer
You should be familiar with basic data concepts and terminology.
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
