Machine Learning and AI
What is the course about?
Are you ready to dive into the world of Machine Learning? This course helps you build a strong foundation, covering supervised learning methods like regression and classification, as well as unsupervised techniques such as clustering and dimensionality reduction.
Hands-on practice will give you the skills needed to tackle real-world challenges. The course ends with a final project where you can apply everything you’ve learned. After completing this course, you’ll be ready to join the Data Circle and continue your learning journey!
Course Details
Classes: Monday and Wednesday, 19:00 - 21:00
Time Invest: 15 hours per week
Timeline: Start Date is 08th of September 2025, End Date is 08th of December 2025 (14-weeks)
Hybrid: Certain events take place in person in the following locations: NRW, Berlin, Hamburg. More information
Why should you take this course?
Content - Learn how to use machine learning models and how to build and apply them. You learn about Regression, Clustering, and Classification models. You use libraries and manage code with Git & GitHub.
Final Project - Apply your knowledge to a real-world Machine Learning project.
Your Start - This course is the perfect starting point for your journey toward becoming a Data Analyst, Data Scientist or Machine Learning Engineer. By the end of the course, you will have a solid foundation in Python, a GitHub portfolio showcasing your projects, and a ReDI Certificate. Afterward, you can advance your skills by enrolling in the Data Circle course.
Industry Experts - The teachers are volunteers from the industry. They are experts in web development and will help you start your journey toward a tech career!
Learning Format
In the two weekly sessions, teachers introduce key concepts to the students and practice them with small exercises and live coding. Next to the two sessions, students are asked to apply the newly learned concepts in weekly homework and a final project.
Weekly Homework
Every Wednesday, students will receive homework to be submitted by Sunday evening. Homework review is part of the Monday session. There is a requirement for students to complete 80% of the homework throughout the course in order to graduate. Homework is not graded.
ReDI Style
This course is about active participation. You will be asked to work independently on weekly homework to apply your newly learned skills. You are in charge of your learning journey. Are you ready to work hands-on and participate actively in the sessions? Then join us!
Course Outline
The Course Outline may change before the start.
1
Kick-Off
Teachers & Students get to know each other
2
Preparation
Workspace Setup
3
Intro to ML
Review of Python, Pandas, NumPy + Matplotlib
4
Intro to ML
ML intro, what are models, high-level course overview Feature engineering usage and why its important
5
Regression
Intro to Regression Regression types, implement univariate, polynomial, multivariate regression
6
Regression
Regularization Lab
7
ML Model Preparation
Model Preparation and Evaluation Under-/Overfitting, Train/Val/Test Split, Resampling Techniques
8
Classification
Classification Theory Confusion Matrix, ROC
9
Clustering
Clustering Techniques, e.g. kmeans, hierarchical clustering, density-based clustering
10
Career Week
Students can participate in a variety of career workshops.
11
NLP
NLP
12
Final Project
Students implement what they have learned in a final project.
13
Final Project
Students implement what they have learned in a final project.
14
Demo Day
Students present their final project.
A typical week
Onsite Activities
Based on your location there are different on-site activities. Find out more below.
If you are located in Berlin and surrounding, we invite you to four on-site community events throughout the semester.
Timeline
June
Open Days
Join Info Sessions to get to know ReDI School & Fall 2025
July
Open Days Application Open
Join Info Sessions to get to know ReDI School & Fall 2025 Complete the application form and finish your prework.
August
Student Interviews
Students are interviewed for the course.
September
Kick-Off Course runs
We kick-off the semester.
October
Course runs
You join the sessions and work on your project.
November
Course runs
You join the sessions and work on your project.
December
Demo Day
Present their final project.
After the course
You’re familiar with Data Science methods and tools
You’ll be able to use Machine Learning Frameworks
You have a Data Science Mindset
You are ready to apply for ReDI’s Data Circle where you deepen your ML skills by working on realistic data & ML projects.
How to Graduate from the Course?
To graduate and receive the ReDI Certificate, we ask you to:
Attended 80% of the sessions (We have a camera on policy)
Submit 80% of the homework and a final project
How to apply?
Sign up to Join Open Day
Optional for current ReDI students. Join us to find out more about the courses and requirements.
Complete Prework
Complete it before the application deadline. Start early! The Prework might take up to 20 hours in total. The deadline is stated in the email with the link to the application form.
Complete Your Prework
Start applying now by completing the Machine Learning and AI Prework!
FAQ
🤖 Unsure about the course?
Try out our AI Chatbot on Open AI (you need a ChatGPT account to access it). Please keep in mind that the Chatbot might make mistakes. You can find all the correct information on the Applicant Hub.
💬 Still unsure what to do..?
You tried our AI Bot - and didn’t find the answer you needed? Please make sure to review the Applicant Hub carefully, your answer is likely there. Still stuck? Check our FAQ Page. Alternatively, you can reach out to us via email: dcp@redi-school.org.
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