Handledningar i data science

A tutorial on mastering the fundamentals of anomaly detection - the concepts, terminology, and code.

Learn about loss functions in machine learning, including the difference between loss and cost functions, types like MSE and MAE, and their applications in ML tasks.

Explore two of the most commonly used programming paradigms in data science: object-oriented programming and functional programming.

With Chroma DB, you can easily manage text documents, convert text to embeddings, and do similarity searches.

Uncover actionable insights hidden in complex signal data by filtering noise, choosing appropriate visualizations, finding patterns in the time- and frequency-domain, and more using signal processing.

Discover the main differences between Python’s pandas and polars libraries for data science

Discover what Git Rebase is and how to use it in your data science workflows.

Learn how to use ChatGPT in a real-life end-to-end data science project. We will use it for project planning, data analysis, data preprocessing, model selection, hyperparameter tuning, developing a web app, and deploying it on the Spaces.

In this Docker tutorial, discover the setup, common Docker commands, dockerizing machine learning applications, and industry-wide best practices.

A complete overview to understanding multiple linear regressions in R through examples.

Write your first bash script and learn the essentials: variables, functions, loops, and conditionals with working code examples.

A comprehensive introductory tutorial that will help you master the fundamentals of Julia. Learn about operators, conditional statements, working with DataFrames, and more.