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Full machine learning course. Topics include: - Representing information and preparing data for use with ML methods - Classifiers and feature importance, including K-nearest neighbors, logistic regression, support vector machines - Decision trees: ensemble methods, random forests, and boosting - Regression and combating over-fitting: ridge regression, lasso, elastic nets, polynomial regression, support vector regression - Nonlinear dimensionality reduction: kernel PCA, local linear embedding, ISOmap, multidimensional scaling - Semi-supervised learning: label spreading, label propagation - Unsupervised learning - Evaluation in ML: metrics, cross-validation, statistics, addressing the multiple comparisons problem Assumptions: programming skills in an object-oriented language. Tools that we use: Python, Pandas, Numpy, Scikit-Learn. Supporting GitHub repository: https://github.com/Symbiotic-Computing-Laboratory/mlp_2022

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