ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Everybody knows you need to clean your data to get good ML performance. How does cleanlab work?

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ML ML Data Scientist AI 88

Flipboard

FEBRUARY 2, 2023

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. Next, we present the data preprocessing and other transformation methods applied to the dataset.

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Cross Validation ML ML Machine Learning 123

AWS Machine Learning Blog

FEBRUARY 10, 2023

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. Each season consists of around 17,000 plays.

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ML ML Machine Learning Machine Learning 77

How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. 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. Sounds great, right?

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Machine Learning Machine Learning Data Quality Algorithm 52

Flipboard

JULY 10, 2023

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 ).

Deep Learning 131

Deep Learning Deep Learning Machine Learning Machine Learning 131

Mlearning.ai

JANUARY 26, 2023

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data. 2018, July). Remote Sensing, 12(24), 4033.

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AWS Database Data Science Clean Data 98

AWS Machine Learning Blog

MAY 5, 2023

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. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017. Then import the relevant modules.

Machine Learning 90

Machine Learning Machine Learning AWS ML 90