ML model evaluation mistakes that make good numbers meaningless
Nine errors behind great offline metrics and bad production models: random splits on time series, threshold-free metrics, tuning on the test set.
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Nine errors behind great offline metrics and bad production models: random splits on time series, threshold-free metrics, tuning on the test set.
The model is the small part. Training-serving skew, feature freshness, shadow deploys, drift detection, and monitoring that catches decay.
Retrieval-augmented generation in plain Python: split a PDF into chunks, find the relevant ones, and let Claude answer using only those. No vector database required.
Asking a model to reply in JSON works until it does not. Learn why tool schemas beat prompt begging, and how to validate every response before it reaches your code.
Three ways to keep project dependencies apart. A straight comparison of speed, package coverage and complexity, plus a clear recommendation for each situation.
A script that sorts files into folders by type, handles name clashes safely, and can keep watching for new arrivals. Dry-run first, so nothing is ever lost.
Ten pandas fixes for the problems every real dataset has: broken headers, duplicate rows, mixed types, silly outliers and inconsistent categories.
Use MediaPipe Pose to measure your elbow angle and count push-up reps automatically. The same state machine works for squats, curls and any repeated movement.
Tesseract plus a few lines of OpenCV preprocessing turns blurry photos into clean text. Learn the cleanup steps that take accuracy from unusable to reliable.
Windows says pip is not recognized as an internal or external command because pip is not on your PATH. Use python -m pip for an instant fix, then repair PATH properly.
A beginner-friendly Python script that renames messy files in a folder — with a safe dry-run mode so nothing breaks.