> For the complete documentation index, see [llms.txt](https://redi-school-1.gitbook.io/applicant-hub/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://redi-school-1.gitbook.io/applicant-hub/data-ai-track/machine-learning-and-ai.md).

# 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!

{% hint style="info" %}

### Course Details <a href="#how-do-i-participate-in-the-courses-self-paced-mode-vs-cohort-mode" id="how-do-i-participate-in-the-courses-self-paced-mode-vs-cohort-mode"></a>

* Classes: Monday and Wednesday, 19:00 - 21:00
* Time Invest: 15 hours per week
* Timeline: Start Date is 14th of September 2026, End Date is 8th of December 2026 (14-weeks)
* Hybrid: Certain events take place in person in the following locations: NRW, Berlin, Metropolitan Region of Hamburg. [More information](#onsite-activities)
  {% endhint %}

{% embed url="<https://www.loom.com/share/09756f8e17ff42bea77507efbec9d3de?sid=285a7359-9f97-4a7d-9be9-3ad7bc3a9709>" %}

## 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.&#x20;
* **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!&#x20;

## 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.&#x20;

## 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.

{% hint style="warning" %}

## **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!&#x20;
{% endhint %}

## Course Outline

*The Course Outline may change before the start.*

<table><thead><tr><th width="165">Week</th><th width="273">Topic</th><th>Content</th></tr></thead><tbody><tr><td>0</td><td>Onboarding</td><td>Get to know ReDI</td></tr><tr><td>1</td><td>Kick-Off</td><td>Teachers &#x26; Students get to know each other</td></tr><tr><td>2</td><td>Preparation</td><td>Workspace Setup</td></tr><tr><td>3</td><td>Intro to ML</td><td>Review of Python, Pandas, NumPy + Matplotlib</td></tr><tr><td>4</td><td>Intro to ML</td><td>ML intro, what are models, high-level course overview Feature engineering usage and why its important</td></tr><tr><td>5</td><td>Regression</td><td>Intro to Regression<br>Regression types, implement univariate, polynomial, multivariate regression</td></tr><tr><td>6</td><td>Regression</td><td>Regularization<br>Lab</td></tr><tr><td>7</td><td>ML Model Preparation</td><td>Model Preparation and Evaluation Under-/Overfitting, Train/Val/Test Split, Resampling Techniques</td></tr><tr><td>8</td><td>Classification</td><td>Classification Theory Confusion Matrix, ROC</td></tr><tr><td>9</td><td>Clustering</td><td>Clustering Techniques, e.g. kmeans, hierarchical clustering, density-based clustering</td></tr><tr><td>10</td><td>Career Week</td><td>Students can participate in a variety of career workshops.</td></tr><tr><td>11</td><td>NLP</td><td>NLP</td></tr><tr><td>12</td><td>Final Project</td><td>Students implement what they have learned in a final project.</td></tr><tr><td>13</td><td>Final Project</td><td>Students implement what they have learned in a final project.</td></tr><tr><td>14</td><td>Demo Day</td><td>Students present their final project.</td></tr></tbody></table>

## A typical week

<details>

<summary>Monday 19:00 - 21:00</summary>

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.

</details>

<details>

<summary>Wednesday 19:00 - 21:00</summary>

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.

</details>

<details>

<summary>Thursday - Monday</summary>

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.

</details>

## Onsite Activities

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

{% tabs %}
{% tab title="Berlin" %}
If you are located in Berlin and surrounding, we invite you to some online and onsite community events throughout the semester.
{% endtab %}

{% tab title="NRW" %}
If you are located in NRW, we invite you to some online and onsite community events throughout the semester.
{% endtab %}

{% tab title="Hamburg" %}
If you are based in the Hamburg metropolitan region, you’ll attend some on-site career events and our Demo Day Celebration in December.
{% endtab %}
{% endtabs %}

## Timeline

<table><thead><tr><th width="177">Month</th><th width="230">Topics</th><th>Description</th></tr></thead><tbody><tr><td>June</td><td>Open Days</td><td>Join Info Sessions to get to know ReDI School</td></tr><tr><td>July &#x26; August</td><td>Open Days<br>Application Open<br>Student Interviews</td><td>Join Info Sessions to get to know ReDI School<br>Complete the application form and finish your prework.<br>Learners are interviewed for the course.</td></tr><tr><td>September</td><td>Kick-Off<br>Course runs</td><td>We kick-off the semester.</td></tr><tr><td>October &#x26; November</td><td>Course runs</td><td>You'll join the sessions and complete project work.</td></tr><tr><td>December</td><td>Demo Day</td><td>You'll present your final project.</td></tr></tbody></table>

## After the course

* You’re familiar with Data Science methods and tools
* You’ll be able to use Machine Learning Frameworks&#x20;
* You have a Data Science Mindset&#x20;
* 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](/applicant-hub/resources/camera-on-policy.md))
* Submit 80% of the homework and a final project

{% hint style="success" %}

## Is this course for me?

* [x] you are interested in creating Machine Learning Models and analyzing data
* [x] you have a solid understanding of Python and Data Analytics (pandas, seaborn)
* [x] you have a first understanding of Machine Learning&#x20;
* [x] you can understand and speak English
* [x] you can commit at least 15 hours a week
* [x] you are eager to work on projects
* [x] you are committed to working in the [ReDI style](/applicant-hub/resources/redi-style.md)
  {% endhint %}

***

## FAQ

<details>

<summary>Not sure which track you are interested in?</summary>

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:&#x20;

* [How to choose a tech career?](https://www.freecodecamp.org/news/how-to-choose-a-tech-career/)
* [Career Changer Playbook](https://ga-core.s3.amazonaws.com/cms/files/files/000/003/816/original/Career-Changers-Playbook.pdf)
* [Career Tech Guide](broken://spaces/Oa1pNW9YA7CW5ZRLDgwP/pages/5UeYVIpDeUCdYECaVbyh)

</details>

<details>

<summary>Not sure which course level to apply for? </summary>

Check out the [Prework](/applicant-hub/resources/prework.md) of the different levels. If you are a little bit challenged but able to complete a Prework, then the level is right for you.

</details>

{% hint style="info" %}

## 🤖 Unsure about which course to choose or have a question?

Try out our [AI Chatbot on Open AI](https://chatgpt.com/g/g-682b35175a5881919fac8d808d8a81ef-redi-school-dcp-course-applicants-advisor) (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.&#x20;
{% endhint %}

***

### [💬](https://emojipedia.org/speech-balloon) Still unsure what to do..?&#x20;

You tried our [AI Bot](https://chatgpt.com/g/g-682b35175a5881919fac8d808d8a81ef-redi-school-dcp-course-applicants-advisor) - 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](https://redi-school-1.gitbook.io/applicant-hub/frequently-asked-questions-faq). Alternatively, you can reach out to us via email: <dcp@redi-school.org>.&#x20;


---

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