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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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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. In this post, we deep dive into the technical details of this ML model.

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[Latest] 20+ Top Machine Learning Projects for final year

Mlearning.ai

We have the IPL data from 2008 to 2017. How to perform Face Recognition using KNN So in this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. We will also be building a beautiful-looking interactive Flask model. Working Video of our App [link] 12.

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[Latest] 20+ Top Machine Learning Projects with Source Code

Mlearning.ai

We have the IPL data from 2008 to 2017. How to perform Face Recognition using KNN So in this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. We will also be building a beautiful-looking interactive Flask model. Working Video of our App [link] 12.

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70+ Best and Unique Python Machine Learning Projects with source code [2023]

Mlearning.ai

We have the IPL data from 2008 to 2017. It can also be thought of as the ‘Hello World of ML world. How to perform Face Recognition using KNN In this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. Working Video of our App [link] 11.