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2 مستودعات

Awesome GitHub RepositoriesML Problem Framing

Processes for defining business objectives and target metrics to classify ML problems.

Distinct from Project Planning: Focuses on framing a machine learning problem specifically, rather than general strategic project planning.

Explore 2 awesome GitHub repositories matching software engineering & architecture · ML Problem Framing. Refine with filters or upvote what's useful.

Awesome ML Problem Framing GitHub Repositories

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • ageron/handson-mlالصورة الرمزية لـ ageron

    ageron/handson-ml

    25,608عرض على GitHub↗

    This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

    Guides the process of establishing objectives and metrics to frame problems as supervised or unsupervised learning.

    Jupyter Notebook
    عرض على GitHub↗25,608
  • mrdbourke/machine-learning-roadmapالصورة الرمزية لـ mrdbourke

    mrdbourke/machine-learning-roadmap

    7,871عرض على GitHub↗

    This project is a technical curriculum and learning path for machine learning, providing a structured sequence of mathematical foundations, core concepts, and professional workflows. It serves as a comprehensive guide and resource index that connects theoretical principles to the specific software libraries and tools used in real-world implementation. The repository functions as a project workflow blueprint, outlining the sequential steps required to solve machine learning problems from initial discovery through to final deployment. It maps theoretical mathematical principles to practical app

    Organizes the sequential workflow steps needed to move a machine learning project from discovery to implementation.

    عرض على GitHub↗7,871
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