Data Science Tutorials

Set up OpenClaw(formerly Clawdbot/Moltbot), a self-hosted agent connecting Claude to your Mac via WhatsApp. Search files and run shell commands from your phone.

Stop vanishing gradients and biased models. Learn how to normalize data using min-max and z-score in Scikit-learn to improve machine learning models.

Running docker run hello-world is the fastest way to confirm your Docker installation works correctly, understand what happens when you execute a container, and troubleshoot common setup issues before building real applications.

A practical guide covering the structure, rules, and real-world examples developers need to write clear, consistent Git commit messages that improve debugging, speed up code reviews, and turn Git history into searchable documentation.

Learn the mathematical foundations of single-event probabilities, explore worked examples from classical statistics to real-world scenarios, and discover applications across data science and machine learning.

Understand how Excel gridlines work, when they appear, and how they are different from borders. Learn to create clear worksheets on screen and in print.

Upgrade to Transformers v5. A practical guide to the unified Rust backend, API changes, and side-by-side v4 vs v5 migration patterns for encoding and chat.

Explore the architecture of Google’s managed MCP servers and learn how to turn LLMs into proactive operators for BigQuery, Maps, GCE, and Kubernetes.

Learn how to automate AWS infrastructure with Terraform. Build EC2 instances with SSM agents, manage state in S3, and scale using modules.

Learn how to clear Excel cache on Windows and Mac to solve common problems like lag and sync errors, and follow best practices to keep Excel running smoothly.

Setting up llama.cpp to run the GLM-4.7 model on a single NVIDIA H100 80GB GPU, achieving up to 20 tokens per second using GPU offloading, Flash Attention, optimized context size, efficient batching, and tuned CPU threading.

Learn practical patterns for working with text files, logs, and large datasets in Python.