Cross-Validation for Small Datasets: What to Do Instead
A single train/test split can be unreliable when data is scarce. Here’s how cross-validation for small datasets fixes that, and a real project example.
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Notes on data engineering, AI and building things end to end.
Data Versioning: Why Datasets Need Version Control Too
Code gets versioned by default. Datasets usually don’t. Here’s why data versioning matters for reproducibility, what to actually track, and a real project example.
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L1 vs L2 Regularization: When to Use Each
L1 and L2 regularization both fight overfitting, but shape the model differently. Here’s L1 vs L2 regularization explained — when each helps, and a real project example.
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Feature Engineering vs Feature Selection: What’s the Difference
Feature engineering vs feature selection is expansion versus reduction — one creates new inputs, the other filters existing ones. Here’s why the order matters, with real project examples.
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Class Weights vs SMOTE: Handling Imbalanced Data
When one class vastly outnumbers another, accuracy alone hides the problem. Here’s class weights vs SMOTE — how each works, which to try first, and a real project example.
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Hyperparameter Tuning Methods: Grid vs Random vs Bayesian
A model’s default settings are rarely its best. Here’s how grid search, random search, and Bayesian optimization compare as hyperparameter tuning methods, and which to actually use.
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Ensemble Methods in Machine Learning: When to Use Them
A single model has a single point of view. Here’s how ensemble methods in machine learning combine multiple models for better results — bagging, boosting, stacking — with a real project example.
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Feature Selection in Machine Learning: How to Choose the Right Inputs
More features isn’t automatically better. Here’s how feature selection in machine learning actually works — the three main approaches, common pitfalls, and real project examples.
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Model Monitoring in Production: What to Watch For After Deployment
A model that passed every test can still quietly fail months later without any code changing. Here’s what model monitoring in production actually watches for, with real project examples.
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How to Detect Overfitting (Beyond the Textbook Definition)
Everyone can define overfitting. Fewer people can actually catch it before it ships. Here’s how to detect overfitting in practice — the real signs, how to test for it, and how to fix it.
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