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| Contents: Introducing machine learning -- Managing and understanding data -- Lazy learning : classification using nearest neighbors -- Probabilistic learning : classification using naive Bayes -- Divide and conquer : classification using decision trees and rules -- Forecasting numeric data : regression methods -- Black box methods : neural networks and support vector machines -- Finding patterns : market basket analysis using association rules -- Finding groups of data : clustering with k-means -- Evaluating model performance -- Improving model performance -- Specialized machine learning topics. |
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