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LO-1
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Upon successful completion of this course, students will gain the competence to understand the basic concepts of machine learning, learning types, and algorithm selection criteria to perform preprocessing steps such as missing data analysis, feature scaling, and data splitting on real-world datasets; successfully apply supervised learning algorithms like linear/logistic regression, decision trees, and support vector machines, as well as unsupervised learning models like K-means and hierarchical clustering; reduce data complexity using dimensionality reduction techniques while evaluating the performance of models with a scientific approach using confusion matrices, cross-validation, and hyperparameter optimization methods, ultimately developing end-to-end effective machine learning projects. |