Data Science Tutorials

Learn what Maximum Likelihood Estimation (MLE) is, understand its mathematical foundations, see practical examples, and discover how to implement MLE in Python.

Explore KL-Divergence, one of the most common yet essential tools used in machine learning.

Discover how to use the Excel UNIQUE() function to instantly remove duplicates and extract distinct values from any list. Learn how to apply it to both rows and columns.

Dive deep into the world of p-hacking, where manipulating the p-value is tempting. Learn the different traps of p-hacking, and how to detect and prevent them.

Learn how git push force rewrites branch history, how to use it safely, and how to recover commits without breaking team workflows.

Learn the various methods of iterating over rows in Pandas DataFrame, exploring best practices, performance considerations, and everyday use cases.

Learn how to make your code modular, testable, and maintainable by understanding and implementing various Python dependency injection frameworks.

This guide covers four installation approaches, from the official Docker repository to manual package installation, plus essential post-installation configuration for enterprise deployments. You'll learn how to secure your Docker environment, troubleshoot common issues, and maintain a production-ready setup.

Learn what ChatGPT Agent is, how to use it, how it handles real-world tasks, and where it still falls short.

Learn how to run Mistral's Voxtral Mini 3B model with vLLM, set up an API, and build a Streamlit-based audio summarizer and Q&A app.

Learn to build full-stack web apps fast with the FARM stack (FastAPI, React, MongoDB). This beginner-friendly guide goes through setup, features, and deployment in one click.

Learn what Make is, how to use it to create AI agents that automate tasks, and how it compares to n8n.