Data Science Tutorials

Learn how to use Cofounder 2 to turn a rough idea into a structured company with a business plan, brand kit, engineering tasks, marketing campaigns, and sales workflows through specialized AI agents.

How random forest regression works, where it fails, and how to evaluate, tune, and interpret it. Includes a Python implementation and model comparison framework.

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.

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.

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.