Describe how a few popular machine learning algorithms work

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Popular machine learning algorithms and their workings

Linear Regression - predicts a continuous output based on input features
Decision Trees - creates a tree-like model to make decisions based on input features
Random Forest - ensemble of decision trees to improve accuracy and reduce overfitting
Support Vector Machines - finds a hyperplane that separates data into classes
K-Nearest Neighbors - predicts output based on the k-nearest data points
Naive Bayes - calculates probability of each class based on input features
Neural Networks - simulates the functioning of the human brain to make predictions
Gradient Boosting - ensemble of weak models that are trained sequentially to improve accuracy

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