Data Science Tutorials

Learn how to run a quarterly review in ChatGPT Work, from source files to a report, spreadsheet, and deck. Hands-on walkthrough with the checks before sign-off.

Learn how to rename columns in pandas with .rename(), df.columns, and .set_axis(), plus Python functions for snake_case and fixes for KeyError and ValueError.

Learn how Unsloth's NVIDIA-optimized gradient checkpointing and QLoRA cut VRAM use when fine-tuning Qwen3.5 4B for medical document OCR.

Use Unsloth Desktop for local AI workflows, including LLM inference, fine-tuning, vision models, image and video generation, OpenCode coding agents, and local APIs from one desktop application.

Learn what minima and maxima are, how local and global extrema differ, and how to find them using critical points, first and second derivative tests, and the closed-interval method.

Run Meta’s Muse Glimmer local agentic model using llama.cpp, dynamic quantization, DFlash speculative decoding, vision support, and OpenCode to run a fast, private, and low-cost AI coding agent.

Learn what an invertible matrix is, how to determine if a matrix is invertible, and why matrix inverses matter in linear algebra and data science.

No, it didn't solve the Riemann hypothesis. But it did stumble into a genuine, Lean-verified improvement on a 50-year-old bound.

Build and deploy a live financial research app on FastAPI Cloud using GPT-5.6 Luna, Olostep web search, tool calling, REST endpoints, and OpenAI Agents SDK tracing. 

Learn how to run fast, accurate, multilingual speech-to-text locally on a CPU using Microsoft VibeASR.cpp, with file transcription, real-time streaming, and a Gradio web interface on Windows, Linux, and macOS.

Learn how to use Grok Voice Think Fast 2.0 to build a real-time voice agent that handles spoken conversations, calls tools, manages interruptions, and resumes disconnected sessions.

This tutorial introduces Oracle AI Vector Search, explains its core features, and walks through a practical local project. It also covers setup requirements, semantic search, vector indexing, and common troubleshooting steps.