Data Science Tutorials

Learn how Claude Code routines run your coding agent in the cloud on a schedule or a GitHub event, so PR reviews and audits finish with your laptop closed.

GGUF packages model weights, tokenizer data, and metadata into a single portable file. Learn how to choose the right quantization level and get started with Ollama.

Learn how to accelerate local Gemma 4 31B inference on a single RTX 4090 using DFlash speculative decoding and Flash Attention against a baseline setup.

A complete tutorial on using Python-native tools for async CRUD operations and HTMX interactivity.

Kernel density estimation is a nonparametric method for estimating the shape of a data distribution without assuming a fixed model. Learn the formula, bandwidth selection, and hands-on implementation in Python and R.

Learn how to install MySQL on Windows, macOS, and Linux. Configure your server, secure it properly, and verify the installation step by step.

A practical walkthrough of the assumptions behind logistic regression, the diagnostics that catch violations in Python and R, and the alternatives to reach for when the assumptions don't hold.

A practical guide to spline regression, covering how piecewise polynomials and knots model nonlinear relationships, the main spline types, and how to fit them in Python and R.

A practical guide to GLMs - what they are, how their three components work together, and how to fit and interpret them in Python and R.

A detailed walkthrough of overfitting and underfitting in machine learning, including how to identify each failure mode, why it happens, and how to fix it through the bias-variance tradeoff.

Learn what zero-shot classification is, how it works under the hood with NLI models, how it compares to few-shot and fine-tuning, and how to apply it with Hugging Face Transformers.

See how an end-to-end MLOps pipeline predicts World Cup 2026 results, from automated retraining and DVC to a 10,000-run Monte Carlo simulation of the bracket.