基础概念

Machine Learning (ML)

定义

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. ML algorithms build models based on training data to make predictions or decisions.

示例

Spam email filtering
Credit scoring
Product recommendations

相关术语

Artificial Intelligence (AI)

A field of computer science focused on creating systems that can perform tasks typically requiring human intelligence. This includes learning from experience, understanding language, recognizing patterns, solving problems, and making decisions.

Supervised Learning

A machine learning approach where the model is trained on labelled input-output pairs. The model learns to map inputs to correct outputs, enabling predictions on new, unseen data. Most commercial ML models in production today are supervised.

Unsupervised Learning

A machine learning approach that discovers structure in unlabelled data. Common techniques include clustering (grouping similar items) and dimensionality reduction. It is often used for anomaly detection and exploratory data analysis.

了解术语只是第一步，将其落地应用才是第二步。

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Machine Learning (ML)

定义

示例

相关术语

Artificial Intelligence (AI)

Supervised Learning

Unsupervised Learning

相关服务

了解术语只是第一步，将其落地应用才是第二步。

Machine Learning (ML)

定义

示例

相关术语

Artificial Intelligence (AI)

Supervised Learning

Unsupervised Learning

相关服务

了解术语只是第一步，将其落地应用才是第二步。