A practical 2026 guide to imbalanced classification in Python: when to reach for SMOTE, ADASYN, BorderlineSMOTE, class_weight, or threshold tuning — with runnable scikit-learn 1.8 and imbalanced-learn 0.14 code, common pitfalls, and a clear decision framework.
A hands-on comparison of KMeans, DBSCAN, and HDBSCAN clustering in scikit-learn 1.8. Working Python code, evaluation metrics, parameter tuning tips, and a decision framework to pick the right algorithm.
Master every cross-validation strategy in scikit-learn 1.8 — KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit, and more. Includes working code, a decision flowchart, and tips for avoiding data leakage.
Build a complete anomaly detection toolkit for time series data in Python. Four hands-on methods — rolling Z-scores, STL decomposition, Isolation Forest, and LOF — with feature engineering, ensemble voting, and production-ready scikit-learn 1.8 code.
Learn how to handle missing data in Python using pandas 3.0 and scikit-learn 1.8. Covers detection, SimpleImputer, KNNImputer, IterativeImputer (MICE), pipeline integration, and a practical decision framework for choosing the right strategy.
Learn how to use SHAP values to explain machine learning predictions in Python. Practical guide with scikit-learn, XGBoost, and LightGBM code examples, visualization techniques, and production-ready interpretability patterns.
Master every major hyperparameter tuning strategy in Python — from GridSearchCV and RandomizedSearchCV to Bayesian optimization with Optuna 4.7. Working code examples, head-to-head comparison, and production best practices.
A hands-on guide to feature engineering in Python using scikit-learn pipelines. From scaling and encoding to custom transformers and feature selection, learn how to build robust, leak-free ML preprocessing workflows.
Scikit-learn 1.8 brings native GPU acceleration, free-threaded Python 3.14 support, temperature scaling, ClassicalMDS, and major performance improvements. A hands-on guide with code examples and migration advice.