Data Science Tutorials

Learn how SARIMA extends ARIMA to handle seasonality, understand its seven parameters, and build a working model in Python from data collection through forecasting.

A hands-on guide to the silhouette score, covering the formula, interpretation ranges, a scikit-learn example, how to use it for choosing the right number of clusters, and how it compares to other clustering metrics.

Learn how to leverage Google's latest multimodal models to build high-performance, cost-effective image and video generation pipelines in Python.

Learn how to implement semantic search in MongoDB with Python. Generate vector embeddings, create a Vector Search index, and run $vectorSearch queries.

Learn how to use Grok in Microsoft Excel to analyze data, generate formulas, clean spreadsheets, create charts, and run financial scenarios using natural-language prompts.

Knowing why spurious relationships happen, from confounders to sampling bias, is what separates a real finding from a statistical coincidence.

A practical guide to hyperparameter tuning, covering how it works, the main search methods (grid, random, Bayesian), evaluation strategies, and how to run it in Python and R.

Learn how to run Bonsai 27B, a compact 1-bit Qwen3.6-27B model, locally using llama.cpp, an OpenAI-compatible API, a web UI, and the Pi coding agent.

A practical walkthrough of RAGFlow, the open-source platform for building production Retrieval Augmented Generation applications, covering its architecture, document parsing engine, retrieval strategies, agent workflows, and how it compares to LangChain and LlamaIndex.

Learn how to use the Claude Opus 5 API in Python. Build a bug-fixing agent with tools, then benchmark all five effort levels on pass rate, tokens, and cost.

Learn how to turn a simple LLM prototype into a reliable, scheduled AI workflow with Apache Airflow.

Learn how to run Thinking Machines' Inkling locally. A complete guide to building experimental llama.cpp, downloading Unsloth GGUFs, and multi-GPU serving.