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AI4Good: Machine Learning for a Better World

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This four-part introductory workshop series will give participants an opportunity to learn about machine learning, culminating in applying Microsoft Azure’s machine learning to a data set that addresses a topic that is important to their community. We will start with introducing the concept of machine learning and artificial intelligence, how it affects Louisville, and the work of the Microsoft Future of Work. As we dive deeper, we will learn how to find an open data set and apply Microsoft Azure machine learning to make predictions about community data. We will also learn about Power BI and how it can be used for data visualization. No previous coding experience is required!

Workshop #1
2/3/2021, Wednesday @ 12-1 PM (EST)

General overview of Microsoft Future of Work, including the history of AI, AI’s various functions, Louisville’s place in automation, and why we should all care. Then, we will be introduced to general machine learning concepts.

Workshop #2
2/10/2021, Wednesday @ 12-1 PM (EST)

In the second workshop, we will delve deeper into the general steps of developing an ML model and become familiar with a dataset from Louisville’s Open Data repository.

Workshop #3
2/17/2021, Wednesday @ 12-1 PM (EST)

In this workshop, we will cover the most important part of data modeling: cleaning your data. We will go over certain techniques and why they are essential to this process.

Workshop #4
2/24/2021, Wednesday @ 12-1 PM (EST)

In the final workshop, we will create our model using the Microsoft Azure Machine Learning platform as well as learning about different data visualizations in PowerBI.

Alisia McClain, Director of Community & Education Initiatives,
Microsoft Future of Work Founder, TECH-Nique, Inc.

Meet Alisia

As Director of Community and Education Initiatives for the Microsoft Future of Work, Alisia McClain is focused on preparing industry & education for the advent of innovative technologies such as artificial intelligence, machine learning, and the internet of things. Her current project engages industry, education, and community around developing a diverse and prepared workforce for the coming data economy revolution. Additionally, she collaborates with educators and industry leaders on how partnerships can be leveraged to empower marginalized communities around data science.

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