Data Science Tutorials

Discover how WHERE filters row-level data in SQL queries, while HAVING filters grouped data after aggregation, and master their distinct uses in SQL querying.

Learn Speculative RAG, a technique that improves RAG through a two-step drafting and verification process, and apply your skills with a hands-on implementation using Hugging Face Transformers.

Understand the SQL order of execution and how its different from the order of writing. Write accurate and optimized queries for improved performance and avoid common mistakes in query design.

Fine-tuning the Llama 3.1 model to solve specialized algebra problems with high accuracy and detailed results using Unsloth.

Minkowski distance is a way of measuring the straight or curved path between two points, depending on a chosen parameter that affects the shape. Keep reading to learn about the fundamentals, applications, and comparisons of Minkowski distance in various fields.

Explore how NiceGUI allows Python developers to create web-based user interfaces effortlessly, with interactive elements and real-time data updates.

Learn how to fine-tune GPT-4o with images by following this step-by-step tutorial, where you’ll discover how to improve GPT-4o’s ability to recognize Georgian churches.

Learn how to use the Llama 3.2 11B vision model with Gradio to create a multimodal web app that functions as a customer support assistant.

Learn how to distill LLMs with OpenAI's distillation tool. This tutorial provides a step-by-step guide using GPT-4o and GPT-4o-mini for generating Git commands.

Llama Stack is a set of standardized tools and APIs developed by Meta that simplifies the process of building and deploying large language model applications.

Discover how to create simple to complex graphics with Python's turtle module. Learn essential commands and build interactive projects that bring your ideas to life. Explore fractals, animations, and creative designs while mastering Python basics.

Customize the GPT-4o Mini model to classify posts from Reddit into "stressful" and "non-stressful" labels.