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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 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 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.
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.
Build production-quality, reproducible models with scikit-learn by tuning hyperparameters correctly and wrapping every preprocessing step and estimator inside a single Pipeline. You will learn to search the hyperparameter space with cross-validation, avoid data leakage, and persist the best model for deployment.
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.
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.
Build your first neural network from the ground up — understand neurons, layers and activations, see how forward pass and backpropagation actually work, and train a real model in Keras/TensorFlow. You will finish able to design, train and validate a simple network on both tabular and image data, and judge when deep learning is genuinely worth it.
Take a trained scikit-learn model out of a notebook and serve it to real users as a reliable HTTP prediction service. You will persist models with joblib, wrap them in Flask and FastAPI endpoints, validate inputs, choose between batch and real-time inference, and version, monitor and containerise the result with basic MLOps practice.
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.
