20 posts · page 1 of 1
How Spotify streams 100M+ songs to 600M+ users. Covers audio streaming, Discover Weekly ML, the squad/tribe model, event-driven architecture, and offline mode.
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.
Master practical feature engineering with encoding, scaling, binning, feature crosses, and text features -- the techniques that matter most for model performance.
Build robust preprocessing pipelines that handle missing values, detect and treat outliers, and balance skewed class distributions with practical Python code.
A practical guide to reducing feature dimensions with PCA, t-SNE, UMAP, and feature selection methods, with Python code and visualization.
A practical comparison of XGBoost, LightGBM, and CatBoost with side-by-side code, performance benchmarks, and guidance on when to use each.
A hands-on guide to hyperparameter tuning with grid search, random search, Bayesian optimization, and Optuna, with code and practical advice.
Learn practical deployment patterns for ML models including Flask and FastAPI serving, ONNX export, and batch vs real-time inference architectures.
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.
Learn how gradient boosting works and build high-performance models with XGBoost, including tuning, feature importance, and early stopping.
Use Optuna for Bayesian hyperparameter optimization with pruning, search spaces, and integration with scikit-learn and XGBoost.
Understand how SHAP values explain individual predictions and global feature importance in any machine learning model.
Understand gradient descent intuitively — the learning rate, convergence, batch vs stochastic vs mini-batch, and optimizers like Adam.
Build linear regression from scratch — the math, gradient descent, cost function, and a NumPy implementation compared to scikit-learn.
Master Random Forests — bagging, feature randomness, hyperparameter tuning, feature importance, and when to use them over other models.
See how models overfit, why it happens, and how L1, L2, dropout, and early stopping fight it without crippling capacity.
Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
Understand why machine learning data is split into three sets, how to choose proportions, and how to avoid leakage that silently inflates scores.
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.
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.