Data Science Tutorials

DuckDB is a powerful analytical engine that lives on your laptop. You can use it to speed up data reading and processing and reduce your pipeline runtimes from minutes to seconds. Follow this hands-on guide to learn how.

Learn how to connect to the OpenAI O1 models through the API and manage API costs by understanding reasoning tokens and how to control them.

Discover how to use the ANOVA test to compare multiple groups means with clear examples, real-world applications, and practical tips for data analysis.

Learn SARSA, an on-policy reinforcement learning algorithm. Understand its update rule, hyperparameters, and differences from Q-learning with practical Python examples and its implementation.

Uncover the significance of the Gaussian distribution, its relationship to the central limit theorem, and its real-world applications in machine learning and hypothesis testing.

To git checkout a remote branch, you first need to fetch the latest changes from the remote repository, then you can checkout the remote branch locally using its full name (e.g., origin/branch-name).

Corrective RAG (CRAG) is a RAG technique that incorporates self-assessment of retrieved documents to improve the accuracy and relevance of generated responses.

Eigenvectors and eigenvalues are essential for understanding linear transformations. This article covers their geometric interpretation, mathematical calculation, and importance in machine learning.

LangGraph Studio is a visual development environment for LangChain’s LangGraph framework, simplifying the development of complex agentic applications built with LangChain components.

Learn how to use OpenAI o1 to build an end-to-end machine learning project from scratch using just one prompt.

Learn how Python automatically manages memory with reference counting and generational garbage collection, and understand how to manually control garbage collection using the gc module.

Explore how Euclidean distance bridges ancient geometry and modern algorithms, with coding examples in Python and R, and learn about its applications in data science, machine learning, and spatial analysis.