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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 of their choosing 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.  No previous coding experience is required!

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

General overview of Microsoft Future of Work, including the history of AI, AI’s various
functions, Louisville’s place in automation, ethics in AI, and why we should all care.

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

This workshop will introduce machine learning and machine learning techniques through the use of open-source web applications, such as, Jupyter Notebooks and  Scikit-learn. No experience necessary.

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

Learn how to leverage data from Louisville’s open data sources. Then understand how to ready your data for machine learning, in order to address a topic that is important to your community.

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

Train your machine learning model using your data, applying predictive and cluster analysis. Participants will learn to use Microsoft Azure’s machine learning drag and drop interface to easily make predictions with their data.


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 and 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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