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A comparative analysis of various machine learning algorithms, including their strengths, weaknesses, and applications. It covers supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks, as well as unsupervised learning algorithms like k-means and hierarchical clustering.
Typology: Exams
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