Data Science Tutorials

A practical overview of contrastive learning - how models learn by comparing similar and dissimilar examples, the loss functions and methods behind it, and how to implement it in PyTorch.

A conceptual guide to the kernel trick - what it is, how it enables SVMs and other kernel-based models, and when to use it over other approaches to nonlinear modeling.

A practical guide to the Kruskal-Wallis test - what it is, how it works, when to use it over ANOVA, and how to run and interpret it in Python and R.

Learn how to use conversational analytics, BigQuery Studio's newest Gemini-powered feature, to build intelligent data agents with natural language queries.

Use the Cursor SDK to run a local agent, then run a cloud agent that fixes a small bug in a GitHub repo and opens a pull request.

Learn how DeepSeek V4's three reasoning modes work, and build a Streamlit comparison app that shows when each mode actually wins on quality, speed, and cost.

Learn to fine-tune NVIDIA Nemotron-3-Nano-4B on a psychology Q&A dataset using an RTX 3090 GPU using LoRA and TRL after downloading the model from Hugging Face.

Learn how to build an OpenAI agent app that runs inside Modal Sandboxes, works with files, executes code, and returns results in this hands-on Python tutorial.

Learn how to use Alibaba's Qwen 3.6 Plus, Python, and the OpenAI SDK to build an automated invoice processing pipeline with native vision and tool calling.

The Mann-Whitney U test is a rank-based nonparametric test for comparing two independent groups when data doesn't meet the normality assumption required by the t-test.

Build a Streamlit benchmark application that tests whether Opus 4.7 self-critique memory actually improves coding performance across high, xhigh, and max effort levels.

Learn how to pair Claude Code Auto Mode with Telegram Channels to run an autonomous write-test-debug loop from your phone.