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Applications of Reguralization in Machine Learning

Regularization in Machine Learning is a fundamental concept that sometimes allows predictions to be made or when traditional methods do not allow it. In this course we explain some of the reasons why it might be necessary to use some form of regularization such as: the curse of dimension, correlation, or algebraic dependency. We will study different algorithms that make use of this important concept such as Ridge, Lasso, Decision Trees, SVM or Boosting.