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

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Why Easier Governance Is Superior Governance

Alation

And those who practice these “old school” governance methods have little confidence in their efficacy: 73% of Ventana research participants stated that spreadsheets were a data governance concern for their organization, while 59% viewed incompatible tools as the top barrier to a single source of truth. And it’s growing in popularity.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

While this data holds valuable insights, its unstructured nature makes it difficult for AI algorithms to interpret and learn from it. According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Clean data is important for good model performance.

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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. Advances in neural information processing systems 32 (2019).

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Present and future of data cubes: an European EO perspective

Mlearning.ai

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw dataCleaned 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. Data, 4(3), 92. Data, 4(3), 94.

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Introduction to Autoencoders

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It works well for simple data but may struggle with complex patterns. Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ).

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Text to Exam Generator (NLP) Using Machine Learning

Mlearning.ai

Finding the Best CEFR Dictionary This is one of the toughest parts of creating my own machine learning program because clean data is one of the most important parts. I also learned and absorbed a lot of things related to AI and more precisely machine learning (ML) including how to train the model, and terms related to that.