Data Science Tutorials

Build a Python image-to-CAD agent with the Kimi K3.1 Pro API using vision input, tool calling, a Docker sandbox, a hidden evaluator, and cost tracking.

Learn what Gemini Embedding 2 is, how it compares to Nova, Voyage, and Qwen, and build a multimodal search app in Python with ChromaDB.

A hands-on Meta Muse setup guide for first-time users. Learn how to set permissions, connect tools, scope a task the agent can actually finish, and read the Activity log so you know what it did before you trust the output.

An autoregressive model predicts future values of a time series from its own past. Learn how to fit one in Python with statsmodels, choose the lag order with PACF and AIC/BIC, and compare it to MA and ARIMA.

A tutorial on the Azure Pricing Calculator, how to use it, and best practices to follow and mistakes to avoid.

Learn how SARIMAX combines seasonal time series modeling with external predictors. Understand its parameters, see how it differs from ARIMA and SARIMA, and build a forecasting model in Python.

Learn how ARIMAX extends ARIMA with external predictors, understand its parameters and assumptions, and build a forecasting model in Python with statsmodels.

Decision models score a fixed set of answers instead of writing text. See how they work, how Jev compares, and what independent tests found.

A hands-on RAG evaluation tutorial in Python. Build an evaluation dataset, score it with the 4 core Ragas metrics, wire a quality gate into CI, generate synthetic test questions, and track scores across commits so you can tell a retrieval failure from a generation failure before your users do.

Learn how to set up Paperclip on Windows with MiMo-V2.6-Pro and build an autonomous AI team that plans, delegates, and manages projects from a single dashboard.

Learn what quadrature is, how numerical integration approximates definite integrals, and how methods such as the trapezoidal rule, Simpson's rule, and Gaussian quadrature work.

Learn how to add on-demand persistent memory to chatbots and agents with MongoDB Atlas Ephemeral Clusters, MongoDB Vector Search, and Automated Embedding.