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Fundamentals of classical machine learning
Classical machine learning involves algorithms that learn from data and make predictions or decisions.
Common algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors.
Key concepts include training data, testing data, model evaluation, and hyperparameter tuning.
Classical ML is often used for tasks like classification, regression, clus
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Bagging and boosting are ensemble techniques used to improve the accuracy of machine learning models.
Bagging involves training multiple models on different subsets of the training data and then combining their predictions through voting or averaging.
Boosting involves iteratively training models on the same data, with each subsequent model focusing on the samples that the previous models misclassified.
Bagging reduces va...
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