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EVENT DETAILS |
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<P><SPAN>Leveraging machines to learn patterns from structured data is extremely powerful. We explore how to apply machine learning concepts in python. Discussed topics include regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization. We also speak about coding best practices, including object-oriented programming, debugging techniques, and visualization techniques like matplotlib. Students learn of the core concepts in machine learning and the ability to efficiently train and benchmark accurate predictive models. They gain hands-on practice with powerful packages like scikit-learn, and through scikit-learn's workflow learn about writing clean, object-oriented Python code. Students build complex ETL pipelines to handle data in a variety of formats and techniques, and practice improving their workflow with tools like feature unions and pipelines that allow them to reduce duplicate work. Finally, students practice tricks like parallelization to speed up prototyping and development.</SPAN></P>
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