Data Science Tutorials

Stop your LLM from forgetting context. Learn how to build an AI agent with episodic, semantic, and procedural memory using Spring AI and Oracle Database.

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

Learn how to set up Cursor automations with this hands-on tutorial covering PR code review, scheduled cron tasks, tools and MCP configuration.

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.

Connect the Claude Fable 5 API to a Python project and build a developer task assistant with structured JSON outputs, streaming, tool use, refusal handling, and a FastAPI endpoint.