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

How to Learn Machine Learning

The quality of your training data in Machine Learning (ML) can make or break your entire project. This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. Why Does Data Quality Matter? Let’s explore some real-world failures.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

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With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. The player tracking data contains the player’s position, direction, acceleration, and more (in x,y coordinates).

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

ODSC - Open Data Science

This process is entirely automated, and when the same XGBoost model was re-trained on the cleaned data, it achieved 83% accuracy (with zero change to the modeling code). 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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Introduction to Autoencoders

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By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ). How Are Autoencoders Different from GANs?

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Visualizing data using t-SNE.” Selvaraju, Ramprasaath R.,

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Create high-quality datasets with Amazon SageMaker Ground Truth and FiftyOne

AWS Machine Learning Blog

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. Connect with the Machine Learning & AI community if you have any questions or feedback!

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Tableau: 9 years a Leader in Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

Tableau

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

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