Data Science Tutorials

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.

Explore the types of structural equation models. Learn how to make theoretical assumptions, build a hypothesized model, evaluate model fit, and interpret the results in structural equation modeling.

Python dlt is a great choice for data engineers who want to move data fast and confidently. It integrates with almost any source and destination and lets you write your ELT/ETL pipelines quickly. This tutorial shows you how to control every step of the pipeline and work with local files, cloud, databases, and REST APIs.

Learn how to install Jenkins on Windows, set up and run Jenkins agents and pipelines, and build MLOps projects with Jenkins pipelines from model training to model serving.

Learn to use Neo4j graph databases with Python: set up AuraDB, write Cypher queries, and ingest data. Master graph database concepts and optimize queries in this comprehensive guide.

Use color_palette() for clear categorical separation, cubehelix_palette() for gradual sequential data, and diverging_palette() for clear divergence from a midpoint.