What is ds-ml-bootcamp?

This repository holds the lessons, code examples, and resources from a one month data science and machine learning bootcamp. It is aimed at people who are starting from zero and want to work through the full process of building a machine learning project, from collecting and preparing data all the way through training a model and putting it into production. The material follows a clear sequence of steps. First you collect data, then you clean and preprocess it, then you split it into training and test sets. After that you choose a model, train it, evaluate how well it performs, and finally deploy it so it can actually be used. The bootcamp presents this as one continuous journey rather than separate topics, so a beginner can see how each stage connects to the next. The stated goal of the program is to move participants from having no practical experience to having built and shipped a real project, all within a single month. It is meant to be hands on: the README suggests cloning the repository and opening it in a tool like Jupyter or VS Code so you can follow along with each lesson while writing and running code yourself, rather than just reading about the concepts. The bootcamp is hosted by two contributors, sharafdin and omartood, who put the lessons together, and it is sponsored by a company called Dugsiiye. Beyond this outline of the workflow and the people involved, the README itself is fairly brief and does not go into detail about specific tools, libraries, datasets, or grading criteria used in the bootcamp. There is no listed programming language for the repository and no separate project description, so it is hard to say from the available information exactly what technical stack the lessons rely on. Anyone interested in the specifics of what is taught week by week would need to look inside the actual lesson files and folders rather than relying on this top level summary, since the README focuses mainly on the overall roadmap and structure of the bootcamp rather than a detailed breakdown of its content.