Data Science Tutorials

Build reliable AI apps faster by turning ad-hoc prompt checks into structured LLM evaluations with Promptfoo, from local test suites to automated CI.

Use four essential commands (add, pull, push, split) to keep shared libraries in sync across teams.

Learn how to check and update your Git version across Windows, macOS, and Linux with easy commands to access the latest features and bug fixes.

A practical guide to `docker init` - what it generates, how to use it with a real Python project, and when it's the right tool for the job.

Solve beginner‑friendly Python exercises, starting with simple variables and building up to functions, data structures, and guided projects.

Master Terraform import of existing cloud resources with CLI commands and import blocks to bring legacy infrastructure under Infrastructure as Code management.

Learn about the definition, formula, key properties, homogeneous coordinates, and applications of affine transformations in graphics, computer vision, robotics, and data preprocessing.

Explore how polynomial regression helps model nonlinear relationships and improve prediction accuracy in real-world datasets.

Learn how Karpathy's AutoResearch runs 100+ ML experiments overnight on a single GPU. Covers the three-file architecture, ratchet loop, results, and limitations.

Learn about LIFO principles, how to implement stacks in Python using lists, deque, and LifoDeque, and apply them for undo/redo systems or graph traversal.

Multi-factor authentication (MFA) protects your MongoDB Atlas account from unauthorized access by requiring verification beyond your password.

Master Python dictionary methods from creation and modification to advanced optimization. Learn error handling and scale performance for production workflows.