📖️ The repository contains materials used during the STRV Data Science Academy

STRV is pleased to offer this 7-week intensive course as part of our STRV Academy. During the course, you will learn to apply plenty of practical and theoretical skills needed for developing and completing your own End-to-End Machine Learning Projects. You will gain practical knowledge and hands-on experience with applied ML techniques and an intuitive understanding of what can be achieved through ML and what its limitations are.

Hearty thanks to our authors: Jan Maly, Niek Mereu, Lukas Koucky, and Jaroslav Bezdek.

✏️ This is the very first run of our DS Academy. We want to make the Academy tremendous, so we will ask for a lot of feedback. If there is a bug or mistake in the implementation, please create an issue on GitHub.

• Learn more about STRV Academy
• All essential materials and information (such as code, theory, guides, and links to resources) are distributed in the form of a handbook
• Follow the Setup to be able to create an environment for code execution

⚠️ There might be some formatting issues when running notebooks. We use a special Markdown to generate the handbook.

The code was tested with conda running on Mackbook M1 (ARM-based CPU). It is generally harder to get Python things running on M1, so the experience on Linux-like and Windows systems should be smoother.

To get your environment up and running for this course, we prepared a guide describing all the necessary steps to replicate our environment.

First off, thanks for taking the time to contribute! 🎉👍 As a participant in the course, there are several things you can help us with.

✏️ If you are an author, refer to the development section.

Bugs are tracked as GitHub issues. Before creating bug reports, please check open issues first, as you might find that you don't need to create one. When creating a bug report, please include as many details as possible. Please fill out the required template; the information it asks for helps us resolve issues faster.

Enhancement suggestions are tracked as GitHub issues. Before creating enhancement suggestions, please check open issues first, as you might find that you don't need to create one. When creating an enhancement suggestion, please include as many details as possible. Fill in the template, including the steps you imagine you would take if the feature you're requesting existed.

MIT