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What Is Supervised Learning? (Definition, Examples) | Built In

What Is Supervised Learning? (Definition, Examples) | Built In

Supervised learning is a type of machine learning that uses labeled data sets to train algorithms in order to properly classify data and predict outcomes.

Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a. Supervised Learning Learns from labeled data. Goal: Predict output for new inputs. Common tasks: Classification, Regression. Examples: Linear Regression, Decision Trees, SVM. b. Unsupervised Learning Learns from unlabeled data. Goal: Discover hidden patterns. Common tasks: Clustering, Dimensionality Reduction. Examples: K-Means, PCA, DBSCAN. c. Semi-Supervised Learning Uses a small amount of labeled data large unlabeled data.

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What Is Supervised Learning? | IBM

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