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Microsoft Future Ready: Designing and Implementing an Azure AI Solution

Cloudswyft via FutureLearn

Overview

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This course is part of the Ethics Laws and Implementing an AI Solution on Microsoft Azure ExpertTrack, helping you understand AI ethics and laws while developing advanced AI and machine learning skills.

During this course, you’ll explore the data science process and how this can be supported using solutions within Microsoft Azure. Alongside industry experts, you’ll delve into the theory of machine learning before gaining hands-on experience to strengthen your knowledge of Microsoft Azure.

This course was created by the Microsoft World Wide Learning team for data scientists, with the aim of supporting the exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Learn how to use automation within your data science projects

Machine learning is transforming many industries, including data science, and is being used to streamline processes for more effective results.

You’ll explore machine learning model training and deployment which will allow you to use a greater degree of automation within your data science projects.

Improve your data modelling with Microsoft Azure

During the course, you’ll be guided by industry experts to review the machine learning capabilities available in Microsoft Azure. You’ll also learn how to use this powerful platform for model selection automation, local model training, model management, and monitoring.

Gain hands-on experience by building a chatbot

Once you have understood the functionality of Microsoft Azure, you’ll gain practical experience by building a simple chatbot.

By putting your knowledge into practice you can solidify what you have learned and also have the opportunity to customise and troubleshoot within the platform.

This course is best suited to aspiring data scientists and those whose job responsibilities primarily fall into the area of data science. This course is designed as one advanced next step from the Introduction to AI course.

Taught by

Daniela Piedrahita

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