# Intro ```{figure} /_static/img/ods_stickers.jpg ``` **
## How to navigate this website and pass the course Here you see a [Jupyter book](https://jupyterbook.org/intro.html) -- an executable book containing MarkDown, code, images, graphs, etc. (we describe Jupyter books in more detail [later](./prereqs/software_devops.md#jupyter-book)). You can jump forward and backward with left and right arrows. Any page can be downloaded as `.md` (MarkDown) or PDF -- use the Download button in the upper-right corner. Additionally, each page containing code can be downloaded as `.ipynb` -- a [Jupyter Notebook](https://jupyter.org) (not to be confused with Jupyter book). For every page, you can see its source on GitHub, and you can also open an issue or suggest an edit -- use the GitHub button in the upper-right corner. OK, let's go! First, check [prerequisites](prereq_python), then you see 10 topics -- from exploratory data analysis with Pandas to gradient boosting. For each topic, there's an introductory part ([here's an example](topic01_intro) for Topic 1) that lists articles to read, lectures to watch and assignments to crack. ## Bonus assignments Additionally, you can purchase a **Bonus Assignments pack** with the best non-demo versions of [mlcourse.ai](https://mlcourse.ai/) assignments. Select the ["Bonus Assignments" tier](https://www.patreon.com/ods_mlcourse) on Patreon or a [similar tier](https://boosty.to/ods_mlcourse/purchase/1142055?ssource=DIRECT&share=subscription_link) on Boosty (rus).