If you are signed up for this tutorial, you will need to be prepared with the following, before you arrive onsite:
This tutorial will target people with basic programming experience to introduce them to the end-to-end analysis of predictive data problems. We will cover the topics in a largely language-agnostic way, drawing on examples from R and Python. The tutorial is comprised of four sections. The last of section will be a hands-on Kaggle competition in which participants can experience firsthand the joys of creating a model and the sorrows of overfitting:
- Identifying opportunities to collect data
- Reading data into a useful format
- Understanding limitations in the data
- Feature extraction
- Basic prediction methods
- Cross validation
- Numerical ways to assess performance
- Showing the results
- Telling a story through visualization
William Cukierski is a data scientist at Kaggle. He has a bachelor’s degree in physics from Cornell University and a Ph.D. in biomedical engineering from Rutgers University, where he studied applications of machine learning in cancer research. Prior to joining Kaggle, he finished competitively in predictive data competitions on topics ranging from predicting stock movements, to forecasting grocery shopping, to automated essay grading.
Ben Hamner is responsible for data analysis, machine learning, and competitions at Kaggle. He has worked with machine learning problems in a variety of different domains, including natural language processing, computer vision, web classification, and neuroscience. Prior to joining Kaggle, he applied machine learning to improve brain-computer interfaces as a Whitaker Fellow at the École Polytechnique Fédérale de Lausanne in Lausanne, Switzerland. He graduated with a BSE in Biomedical Engineering, Electrical Engineering, and Math from Duke University.
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