Birchwood University 4 credits MDS
This course explains the foundational concepts of machine learning, identifying its significance and diverse applications in today's data-driven world. Students differentiate between supervised and unsupervised machine learning, applying train-test splitting for robust model evaluation. Learners identify regression problems, applying simple and multiple linear regression with model assumptions and regularization, and explain logistic regression and the sigmoid function while calculating classification evaluation metrics. The course applies K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), compares ensemble learning techniques including decision trees, random forests, bagging, boosting, AdaBoost, Gradient Boost, and XGBoost, and applies clustering techniques such as K-Means and Hierarchical Clustering, along with Principal Component Analysis (PCA) for dimensionality reduction.
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