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No-Code Machine Learning

No-Code Machine Learning (No-Code ML) refers to platforms and tools that enable users to build, train, and deploy machine learning models without writing traditional code. These platforms typically offer drag-and-drop interfaces, pre-built templates, and automated workflows to simplify tasks like data preprocessing, model selection, and evaluation. They aim to democratize AI by making machine learning accessible to non-technical users, such as business analysts, marketers, and domain experts.

Also known as: No-Code AI, Codeless Machine Learning, Drag-and-Drop ML, Automated ML, Low-Code ML
🧊Why learn No-Code Machine Learning?

Developers should learn No-Code ML when working in cross-functional teams to accelerate prototyping, automate repetitive ML tasks, or enable non-technical stakeholders to contribute to AI projects. It is particularly useful for rapid experimentation, proof-of-concept development, and scenarios where quick insights from data are needed without deep coding expertise, such as in small businesses or educational settings.

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