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Machine learning techniques for data cleaning

Description

PyData Chicago 2016

Slides: https://docs.google.com/presentation/d/1k42esoWoc_WezfPfQ5vxbHTsuFOvAshEusD-GFCElTQ/edit#slide=id.g166bf446d8_1_12

Often, the most interesting datasets - data about people and organizations - are the messiest and most difficult to analyze. When data comes from multiple sources, or when data is entered manually, variation & ambiguity are inevitable. Learn about ways to infer structure and relationships in messy data, using open source Python libraries.

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