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 10th of March 2025, End Date is 19 of June 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.

Week	Topic	Content
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

Monday 19:00 - 21:00

Every Monday from 19:00 to 21:00, you have an online session in which you discuss your homework and where you will be introduced and practice new concepts.

Wednesday 19:00 - 21:00

Every Wednesday from 19:00 to 21:00, you have an online session where the volunteer teachers introduce you to new concepts. They will share the weekly homework with you in this session.

Thursday - Monday

You work on your weekly homework. That means you will be coding hands-on by yourself! If you run into problems, you can contact your class on Slack. You upload your homework before the Monday session.

Onsite Activities

Based on your location there are different on-site activities. Find out more below.

Berlin

If you are located in Berlin and surrounding, we invite you to four on-site community events throughout the semester.

NRW

If you are located in NRW, we invite you to four on-site community events throughout the semester. The Demo Day will also take place in person in Düsseldorf.

Hamburg

If you are based in Hamburg, you will join the course four times in person: Onboarding, two regular course sessions, and Demo Day.

Timeline

Month	Topics	Description
January	Open Day Apply	Complete the application form and finish your prework.
February	Student Interviews	Students are interviewed for the course.
March	Kick-Off Course runs	We kick-off the semester.
April	Course runs	You join the sessions and work on your project.
May	Course runs	You join the sessions and work on your project.
June	Course runs Demo Day	The course finishes! Students 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

Is this course for me?

• you are interested in creating Machine Learning Models and analyzing data

• you have a solid understanding of Python and Data Analytics (pandas, seaborn)

• you have a first understanding of Machine Learning

• you can understand and speak English

• you can commit at least 15 hours a week

• you are eager to work on projects

• you are committed to working in the ReDI style

How to apply?

1

Join Open Day

Optional for current ReDI students. Join us to find out more about the courses and requirements.

2

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.

3

Hand in Application Form

We ask you for the link to your completed prework in the application form.

4

Interview (24.02 - 28.02)

Depending on your course, you may be interviewed by the teachers.

5

Onboarding (10.03)

Your course starts!

Complete Your Prework

Start applying now by completing the Machine Learning and AI Prework!

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FAQ

Not sure which track you are interested in?

If you don't have any experience with tech, apply to our introduction course: HTML & CSS, Infrastructure Basics, Python Foundations or UX/UI Design Bootcamp. To understand which tech career interests you, check out the following link:

• How to choose a tech career?
• Career Changer Playbook
• Career Tech Guide

Not sure which course level to apply for?

Check out the Prework of the different levels. If you are a little bit challenged but able to complete a Prework, then the level is right for you.

🤖 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.

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💬 Still unsure what to do..?

You tried our AI Bot - and it couldn't fully help you ..? In that case, please go through the Applicant Hub thoroughly. The answer is probably in here 🎉. Alternatively, you can reach out to us via email: [e-mail].