Course Overview
Last updated
Last updated
Welcome to ReDI School! We really appreciate that you are part of our community. In this page, you'll find an overview of the course. By volunteering, you contribute to our main goal: help our students gain the necessary skills to find a job in tech. ReDI School has now helped over 17.000 people advance their tech skills. This is only possible with the support of our volunteers <3
Our student community brings together people from over 138 countries. Your course won't be different. Your students will come from a wide range of countries. They also come from diverse professional backgrounds - some are currently unemployed or underemployed, while others are students looking to prepare for their careers. With an average age of 32, many of our students hold a university degree and have several years of work experience. What unites all students is their passion for technology and their aim to build a career in the tech industry.
❤️ Thank you for supporting our students as they take another step in their journey! To learn more about ReDI students and our community, visit .
The Machine Learning & AI Course is designed for students who already have a solid foundation in Python and data analytics and want to expand their expertise in machine learning, AI techniques, and model evaluation. Each cohort consists of 25 students who meet twice a week (Mondays and Wednesdays from 19:00-21:00). The course follows a project-based learning approach, allowing students to work on real-world machine learning applications.
Students progress through three key learning phases:
Machine Learning Fundamentals – Covers data preprocessing, feature engineering, and model training using libraries like Scikit-learn and TensorFlow.
Supervised & Unsupervised Learning – Introduces regression, classification, clustering, and dimensionality reduction techniques.
Final Capstone Project – Students develop their own ML model, applying it to a real-world dataset and presenting their findings on Demo Day.
Each phase includes weekly exercises, coding assignments, and a recap week to reinforce learning before advancing to the next stage.
The weekly schedule consists of two regular classes. Monday begins with a review of the previous week’s content. Teachers provide feedback on completed assignments and discuss solutions. On Wednesday, teachers introduce new ML topics, such as such as regression, classification, clustering, and neural networks. Students engage in hands-on coding demonstrations and guided exercises. The session concludes with the assignment of the weekly homework task.
Between sessions, students are expected to dedicate 10-12 hours per week to coding assignments, and independent study.
As a session owner, you lead the Input session. You introduce the milestone of the week and the relevant concepts to work on it. You prepare the session and coordinate with the teaching assistant.
4 hours per week
As a teaching assistant, you support the session owner in the input session. You open the Zoom call, track attendance, help answer questions, and provide support in break-out rooms.
3 hours per week
As a backup teacher, you are available and ready to jump in the case one of the teachers assigned for the day should have issues, or get sick. As a backup teacher, you don’t need to attend the session unless an emergency arises.
2 hours per week
As a homework reviewer, you correct assignments between Monday 7pm and Wednesday. You do this asynchronously and do not need to attend any sessions.
3 hours per week
We are organizing on-site activities in our three main locations. Find out more below.
Four in-person sessions are taking place in Hamburg: Onboarding, two regular course sessions, and Demo Day. If you are based in Hamburg, it would be fantastic if you could support the course in one or more of these sessions.
The course has two online sessions per week. For on-site events, check out . The session format differs from what you might have seen before.
We hope that by reading this, you have a better idea of the course and what it means to volunteer at ReDI. Let's explore the now 📅.