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Data Science: Supervised Machine Learning in Python
It’s important to know both the advantages and disadvantages of each algorithm we look at.
Jan 6, 2017
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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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Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a.
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