Machine Learning w/ March Madness Data

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Machine Learning w/ March Madness Data-0
Machine Learning w/ March Madness Data-1
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60 min
Private Course
Machine Learning w/ March Madness Data-0

Want to learn to build machine learning models with real-life data? Join me as we make predictions on the NCAA March Madness games.

What you'll learn

Have you ever wanted to learn machine learning using real-world data? Our online class is perfect for you! We use the March Madness dataset to give you a hands-on introduction to machine learning. You'll explore the data, develop models to predict winning teams, and understand player evaluations. Our program gives you the knowledge and confidence to build your own algorithms for any situation.

This class is perfect for

Anyone interested in learning machine learning concepts or developing their machine learning skills.

Listing availability

(Timezone: Etc/UTC)

Jan 01-07, 1970

What to bring

Internet access and a computer/laptop.
Private sessionUp to 1 seats
Private sessionUp to 1 seats

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Learn ,data-science.deep-learning-and-machine-learning, with Uohna T | Amphy
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Uohna Thiessen, Ph.D.

Your teacher

Uohna Thiessen, Ph.D., is a consultant data scientist at Accenture in AdTech practice, working with one of the top-performing corporate tech companies (MAANG). She teaches statistics, quantitative analysis, data science, and machine learning at various U.S. institutions, including the California Institute of Technology and the Flatiron School in NYC. Machine learning models she developed in sectors such as medicine, advertising, and financial technology were designed in R, Python, and SPSS, as well as MS Azure, Google TensorFlow, and Apache PySpark. Uohna holds a Ph.D. in Epidemiology (biostatistics concentration) from Walden University and a B.Sc in biochemistry from Oakwood University. She is certified in Cloud computing and machine learning engineering through Google (GCP) and Microsoft (Azure). Dr. Uohna Thiessen is a data scientist and data science educator whose mission is to provide access to data science education to as many people as possible.

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