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K-Nearest Neighbors: Classification and Regression

Course video 10 of 35

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.

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課程、專項課程和在線學位均由全世界一流大學和教育機構的頂尖授課教師教授。

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