2 repositorios
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
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- ageron/handson-ml
ageron/handson-ml
25,608Ver en GitHubThis 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 - mrdbourke/machine-learning-roadmap
mrdbourke/machine-learning-roadmap
7,871Ver en GitHubThis 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.
