2018, Clean Data and Supervised Learning

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ODSC - Open Data Science

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A recent report by Cloudfactory found that human annotators have an error rate between 7–80% when labeling data (depending on task difficulty and how much annotators are paid). Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms that now power ML applications at hundreds of companies.

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The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

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Real-Life Examples of Poor Training Data in Machine Learning Amazon’s Hiring Algorithm Disaster In 2018, Amazon made headlines for developing an AI-powered hiring tool to screen job applicants. Data Cleaning To ensure model success, it’s crucial to clean data thoroughly, eliminating noise, bias, and inaccuracies.

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Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI

The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

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