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Learn to find structure in unlabelled data with scikit-learn — group similar records with k-means, hierarchical clustering and DBSCAN, judge the result with proper metrics, then compress and visualise high-dimensional data with PCA and t-SNE/UMAP. You will finish able to take a raw feature table and honestly answer "what natural groups and low-dimensional structure live in here?".
Build classifiers for real problems with Python and scikit-learn — logistic regression, k-NN, decision trees, random forests and an overview of SVMs. You will learn the fit/predict workflow, read probability outputs, handle class imbalance, and work a full classification example end to end.
Learn to evaluate machine-learning models rigorously and honestly with Python and scikit-learn. You will build trustworthy train/validation/test workflows, read confusion matrices, choose the right metric for each problem, and diagnose over- and underfitting so your reported numbers reflect real-world performance.
