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Build and interpret regression models in Python with scikit-learn — from a single-feature line to multiple, polynomial and regularised models. You will fit models, read their coefficients, and judge them honestly with MAE, RMSE and R2 so you can predict continuous values and explain what drives them.
Build the core data-handling skill every ML project depends on: loading, exploring and manipulating real datasets with NumPy and pandas. You will vectorise numerical work, wrangle DataFrames, clean missing values, merge tables and produce quick exploratory plots — the everyday groundwork before any model is trained.
Learn what machine learning actually is, how the end-to-end workflow fits together, and how to frame a real problem as a machine-learning task. You will leave able to decide whether ML is the right tool, name the task type, identify features and labels, and set up a working Python environment with scikit-learn and Jupyter.
Turn raw, messy data into clean, model-ready features using pandas and scikit-learn. You will clean and impute data, encode categoricals, scale numerics, handle outliers, engineer and select features, and assemble it all into a leak-free ColumnTransformer pipeline you can trust on unseen data.
