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2 dépôts

Awesome GitHub RepositoriesCloud Machine Learning Examples

Practical demonstrations of machine learning workflows implemented in cloud environments.

Distinguishing note: Focuses on cloud-based ML scenarios rather than general cloud computing.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Cloud Machine Learning Examples. Refine with filters or upvote what's useful.

Awesome Cloud Machine Learning Examples GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • microsoft/data-science-for-beginnersAvatar de microsoft

    microsoft/Data-Science-For-Beginners

    35,657Voir sur GitHub↗

    This project is a comprehensive educational curriculum designed to teach the fundamental concepts, workflows, and tools of data science. It provides a structured learning path that covers the end-to-end data science lifecycle, including data acquisition, maintenance, processing, and pattern discovery, while grounding theoretical knowledge in practical, real-world applications. The curriculum distinguishes itself through a data-driven pedagogical design that utilizes interactive, notebook-based lessons. By combining narrative text with live code blocks, the platform allows learners to experime

    Provides tangible scenarios for applying machine learning techniques in cloud environments.

    Jupyter Notebookdata-analysisdata-sciencedata-visualization
    Voir sur GitHub↗35,657
  • ageron/handson-ml3Avatar de ageron

    ageron/handson-ml3

    13,463Voir sur GitHub↗

    This repository serves as a comprehensive educational resource for mastering machine learning and deep learning through a series of interactive Jupyter Notebooks. It provides a structured collection of tutorials and code examples designed to guide users through the fundamental and advanced techniques of the Python data science ecosystem. The project distinguishes itself by offering hands-on exercises that demonstrate the full lifecycle of machine learning projects. Users can explore end-to-end data pipelines, ranging from initial data loading and preprocessing to the training and deployment o

    Facilitates prototyping and experimentation with machine learning models in remote cloud environments.

    Jupyter Notebook
    Voir sur GitHub↗13,463
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