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  1. 18 de jul. de 2024 · UCI Machine Learning Repository A collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. IPUMS

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  2. Hace 3 días · UCI Machine Learning Repository A collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.

  3. Hace 4 días · UCI Machine Learning Repository. A collection of datasets and data generators used by the machine learning community. Currently has >600 datasets, searchable by data type, task of interest, domain area, and other attributes. Kaggle datasets. Search by size (GBs), file type, license type, and topic/domain tags. MNIST.

  4. 18 de jul. de 2024 · Kaggle provides a free, cloud-based environment where you can access GPU resources, handle large datasets, and collaborate with a diverse community of data scientists and machine learning enthusiasts. Kaggle is a great choice for training and experimenting with Ultralytics YOLOv8 models. Kaggle Notebooks make using popular machine-learning ...

  5. 2 de jul. de 2024 · Machine Learning Data Repositories. UCI Machine Learning Repository: A collection of databases, domain theories, and data generators used by the machine learning community to empirically analyze machine learning algorithms. It has been widely used by students, educators, and researchers worldwide as a primary source of machine ...

  6. Hace 5 días · Hands-on practice is crucial. Working on real datasets and creating various types of visualizations, like using open data sources like Kaggle, UCI Machine Learning Repository, or government databases.Then move on to create visualizations by starting with simple charts and progress to more complex ones. … ver más

  7. Hace 2 días · In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested.