Data Science Tutorials

Learn how to run powerful decision models locally, explore the vLLM ecosystem, and build three practical projects using text, images, and videos.

Build a GitHub CI repair workflow with DeepSeek Superagent, then use observe mode, pytest, holdout data, and human review to check one pandas fix.

Build a Python agent with Gemini 4 Argon, function calling, and profiling, then check its patches with hidden tests, benchmarks, and API cost.

Learn how Mercury 3 uses diffusion-based generation to build an incident analysis assistant, visualize refinement states, and compare low vs. high reasoning effort.

Use Meta Muse Wonder and Olostep web API to check medical claims, explain the reasoning, and show where the evidence came from.

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.