Microsoft Future Ready: Using Python Programming to Explore the Principles of Machine Learning

This course is part of the Advanced and Applied AI on Microsoft Azure ExpertTrack, helping you develop AI and machine learning skills and prepare you for the relevant Microsoft Microcredentials.

Take your knowledge of machine learning and Python programming to the next level with this course offering both theoretical and practical experience.

During this data science course, you’ll gain a strong understanding of the theories of machine learning before enhancing your practical knowledge by building, validating and deploying machine learning models.

You’ll also learn how to use Python programming and Azure Notebooks to help you build and derive insights.

Delve into AI concepts and basic machine learning

Build your understanding of relationships in complex data through basic machine learning and AI concepts.

You’ll learn the theories which drive AI technology today as well as the core principles of machine learning categories including regression techniques and how algorithms behave and learn in machines.

Learn how to deploy machine learning models

This course will help bridge the gap between IT and data science in putting a model into production, teaching you how to effectively deploy machine learning models.

Gain practical experience using Python programming and Azure Notebooks

You’ll understand the importance of evaluating your data before developing algorithms and also get hands-on experience of using Python and Azure Notebooks to evaluate data. These powerful tools will help you gather insights from machine learning models once they have been deployed.

During the course, you’ll learn how to clean data sets, collect output data, request rates, responses, failure rates and more with Python and Azure Notebooks.

This course is for anyone looking to build their understanding of AI and machine learning.

This course is for anyone looking to build their understanding of AI and machine learning.

• Data exploration, preparation and cleaning
• Supervised machine learning techniques
• Unsupervised machine learning techniques
• Model performance improvement