Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

Learn how to train, test, and deploy a machine learning model by using environments as part of your machine learning operations (MLOps) strategy.

Explore the use of generative artificial intelligence (AI) models for natural language processing (NLP) in Azure Machine Learning.

Learn how to automate code checks whenever you update code for machine learning workloads.

Designing reports for enterprise scale requires more than just connecting to data. Understanding semantic models and strategies for scalability and optimization are key to a successful enterprise implementation. This learning path helps you prepare for the Fabric Analytics Engineer Certification.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

This course introduces key concepts to proficiently work with data - data cleaning, preprocessing, manipulation, transformation, and core concepts of data visualization and interpretation. You'll also work through examples with a provided dataset.

R is a widely used programming language for statistical computing. This course will teach you the fundamentals of understanding data types and data structures and how to work with them within R.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

This course will show you how to integrate spatial data into your Python Data Science workflow.

This course focuses on how to represent a graph using three common classes of graph algorithms - the topological sort to sort vertices by precedence relationships, the shortest path algorithm, and finally the spanning tree algorithms.

As working with huge numeric datasets becomes the norm, using the right tools and libraries to work with the data becomes very important. NumPy allows data analysts and data scientists to work with multi-dimensional data to solve these problems.