Data Science Tutorials

Learn simple linear regression. Master the model equation, understand key assumptions and diagnostics, and learn how to interpret the results effectively.

Learn the Adagrad optimization technique, including its key benefits, limitations, implementation in PyTorch, and use cases for optimizing machine learning models.

Learn how to use Mistral’s Pixtral 12B interactively via Le Chat or programmatically through the API available on La Plateforme.

Learn how to calculate sample standard deviation and understand its significance in statistical analysis. Explore examples and best practices for real-world data interpretation.

Create a Tableau donut chart by following simple steps for importing data, resizing, and customizing colors. Learn formatting tips and advanced options. See their advantage over pie charts.

A transitive dependency occurs when one attribute in a database indirectly relies on another through a third attribute, causing redundancy. Keep reading to learn how to identify and eliminate transitive dependencies in database design .

Isolation Forest is an unsupervised machine learning algorithm that identifies anomalies or outliers in data by isolating them through a process of random partitioning within a collection of decision trees.

Discover GitHub Codespaces, the development environment that allows you to write, run, and deploy your code anywhere.

Learn how to use Flux AI to generate images and explore the features, applications, and use cases of each model in the Flux family: Flux Pro, Flux Dev, and Flux Schnell.

Learn how to make the most of NotebookLM by utilizing its podcast-generating feature and its ability to organize, summarize, and answer questions about your documents.

Learn how to calculate the total sum of squares (SST), regression sum of squares (SSR), and error sum of squares (SSE) to evaluate regression model accuracy. Discover their mathematical relationships and how they impact R-squared.

Explore how bootstrapping improves the estimation of confidence intervals and standard errors. Learn to distinguish between parametric and non-parametric bootstrapping techniques, and learn about bootstrapping in time series forecasting.