Skip to content
Codeloom

Topics / Machine Learning

Machine Learning

Machine learning fundamentals. Models, training, and evaluation.

Browse 33 tutorials → Other topics

Why learn Machine Learning?

  • The intuition transfers to every modern AI tool.

  • A high-leverage skill across product, analytics, and engineering.

  • You don't need a PhD — scikit-learn gets you 80% of the way.

  • Pairs naturally with Python, SQL, and Pandas.

What you can build with Machine Learning

Classification and regression on tabular data Forecasting and anomaly detection Recommendation systems Working with embeddings and LLM outputs

Machine Learning tutorials

33 articles · page 1 of 2

Hand-written tutorials, ordered as a recommended learning path.

  1. 01 What Is ML? A clear introduction to machine learning for developers — supervised vs unsupervised, features and labels, train/test split, when ML beats rules, and a tiny scikit-learn example.
  2. 02 Train/Test & Metrics Why splitting matters, how to use train_test_split with stratification, and the metrics that actually matter — accuracy, precision, recall, F1, confusion matrices, and ROC-AUC.
  3. 03 Cross-Validation Master every cross-validation strategy from basic k-fold to nested CV with working Python code and clear guidance on when to use each approach.
  4. 04 Feature Engineering Master practical feature engineering with encoding, scaling, binning, feature crosses, and text features -- the techniques that matter most for model performance.
  5. 05 Preprocessing Pipeline Build robust preprocessing pipelines that handle missing values, detect and treat outliers, and balance skewed class distributions with practical Python code.
  6. 06 Dim. Reduction A practical guide to reducing feature dimensions with PCA, t-SNE, UMAP, and feature selection methods, with Python code and visualization.
  7. 07 Boosting Compared A practical comparison of XGBoost, LightGBM, and CatBoost with side-by-side code, performance benchmarks, and guidance on when to use each.
  8. 08 Tuning Guide A hands-on guide to hyperparameter tuning with grid search, random search, Bayesian optimization, and Optuna, with code and practical advice.
  9. 09 Deployment Patterns Learn practical deployment patterns for ML models including Flask and FastAPI serving, ONNX export, and batch vs real-time inference architectures.
  10. 10 Evaluation Metrics Master every classification and regression metric from accuracy and F1 to AUC-ROC and confusion matrices, with Python code and guidance on when to use each.
  11. 11 Ensemble Methods Master stacking, blending, and voting ensembles to combine multiple ML models for better predictive performance.
  12. 12 MLflow Tracking Track ML experiments, compare model runs, and manage model versions using MLflow's tracking, registry, and artifact features.
  13. 13 XGBoost Learn how gradient boosting works and build high-performance models with XGBoost, including tuning, feature importance, and early stopping.
  14. 14 Optuna Tuning Use Optuna for Bayesian hyperparameter optimization with pruning, search spaces, and integration with scikit-learn and XGBoost.
  15. 15 FastAPI Deployment Deploy machine learning models as production REST APIs using FastAPI with input validation, async inference, and health checks.
  16. 16 SHAP Values Understand how SHAP values explain individual predictions and global feature importance in any machine learning model.
  17. 17 Gradient Descent Understand gradient descent intuitively — the learning rate, convergence, batch vs stochastic vs mini-batch, and optimizers like Adam.
  18. 18 Linear Regression Build linear regression from scratch — the math, gradient descent, cost function, and a NumPy implementation compared to scikit-learn.
  19. 19 Random Forests Master Random Forests — bagging, feature randomness, hyperparameter tuning, feature importance, and when to use them over other models.
  20. 20 Bias Variance The bias variance tradeoff explained with intuition, examples, and practical guidance on how to diagnose and reduce each component of error in your ML models.