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Machine Learning algorithms are different from conventional algorithms as they automatically improve through experience. They traditionally accomplish this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience. Finally, over- and underfitting are discussed, and strategies to avoid them, such as regularization, are explained. |
