Data Science Tutorials

Ready to start with dbt and take data modeling to the next level? This hands-on tutorial dives into essential techniques and project patterns to help you build intuitive, high-quality data warehouses with dbt.

Discover how breadth-first search systematically explores nodes and edges in graphs. Learn its level-by-level approach to ensure the shortest path in unweighted networks. Apply BFS across data science, AI, and networking fields.

Learn how to predict the winner of the 2024 US presidential election using Python, machine learning, and data from FiveThirtyEight and the Federal Election Commission.

Learn how to use Runway Act-One through both the web interface and its API, and discover how to combine it with Runway Gen3-Alpha.

Explore the different methods for filtering out and permanently removing duplicate rows using SQL. Learn the practical applications of how to remove duplicates in SQL Server, MySQL, and PostgreSQL.

Learn how to streamline your LLM evaluations with MLflow. This guide covers MLflow setup, logging metrics, tracking experiment versions, and comparing models to make informed decisions for optimized LLM performance!

Learn how to model and solve optimization problems using Pyomo, a powerful Python library. Explore practical examples from linear and nonlinear optimization!

Explore the arithmetic mean's role in data analysis. Learn its formula, applications, and how it compares to other kinds of means and other statistical measures, and understand when each is most useful.

Uncover the impact of multicollinearity on regression models. Discover techniques to detect multicollinearity and maintain model reliability. Learn how to address multicollinearity with practical solutions.

Learn about the RMSprop optimization algorithm, its intuition, and how to implement it in Python. Discover how this adaptive learning rate method improves on traditional gradient descent for machine learning tasks.

Machine learning is complex and often hard to wrap your head around. By visualizing machine learning models, you can get a great level of understanding of model performance and the decisions the model makes when making predictions.

Discover how the AdamW optimizer improves model performance by decoupling weight decay from gradient updates. This tutorial explains the key differences between Adam and AdamW, their use cases and provides a step-by-step guide to implementing AdamW in PyTorch.