The Data Incubator December Data Science Training with Federal Reserve Boar...

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DC 20009

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This is a private training, led by The Data Incubator, for employees of the Federal Reserve Board.



Course Content

Introduction to Machine Learning

In a world with abundant data, leveraging machines to learn valuable patterns from structured data can be extremely powerful. We explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization. Through powerful packages such as Scikit-learn, students come away with a strong understanding of core concepts in machine learning as well as the ability to efficiently train and benchmark accurate predictive models. They gain hands-on experience building complex ETL pipelines to handle data in a variety of formats, developing models with tools like feature unions and pipelines to reduce duplicate work, and practicing tricks like parallelization to speed up prototyping and development.



About the instructors:

Tim Schwuchow is a Data Scientist in Residence at The Data Incubator. Prior to joining The Data Incubator, he earned degrees in economics from Harvard and Duke. Professionally, he has designed and instructed several undergraduate- and graduate-level courses at Duke, worked as an analyst at D. E. Shaw & Co., a quantitative hedge fund, and worked as an economist for the Postal Regulatory Commission.

Russell Martin is a Data Scientist in Residence at The Data Incubator. Russ has was his PhD in Applied Mathematics from Georgia Tech. After completing his degree, he worked for seventeen years in the United Kingdom, including roles at Warwick University and the University of Liverpool. Russell teaches our Data Science Fellowship and leads corporate trainings for TDI’s training partners.

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DC 20009

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