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 {{see also|Machine learning terms}}
 ==Introduction==
-Machine learning is a branch of artificial intelligence that seeks to develop algorithms and statistical models that enable computers to perform tasks without explicit programming. Training plays an integral role in this process, as it enables the algorithm to learn from data and make predictions based on patterns it detects. In this article, we'll examine training in machine learning in depth - its purpose, different types of training, and its role within it.
+In [[machine learning]], [[training]] enables the [[model]] to learn from [[data]]. The goal of training a model is to determine the optimal [[parameters]] ([[weights]] and [[biases]]) for the model so it can [[accurate]]ly make predictions when presented with new data.
 ==Purpose of Training==
-Training in machine learning is the primary goal of this process, which allows the algorithm to learn from data provided. This involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen information. Training allows the algorithm to generalize from what it has seen so that it can accurately make predictions even when presented with unknown variables.
+Training involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen data. Training allows the algorithm to generalize from what it has seen so that it can accurately make predictions even when presented with unknown variables.
 ==Types of Training==