How do I choose a course?

I am confused and not sure about which course I should choose.

On our EDU-Platform each course and its contents is described but often it will still be difficult to decide what's the best course for your ability level and your needs. Below we therefore included a quick comparison on the courses, which might provide some additional help.

However, if you have any doubt, we strongly recommend you to

attend the Machine Learning Degree info event that is held before the start of each semester, where you get in-depth information on the different courses.

This semester the event will take place on March, the 22nd.

Quick Comparison of the Different Courses

Roughly, the difficulty level of the courses is increasing from left to right.

Einfühurng in Data Science

Einführung in Data Science und maschinelles Lernen

english version below

Der Einführungskurs ist offen für alle. Du wirst abgeholt wo Du bist und begleitet, bis Du in der Lage bist, dein eigenes Projekt durchzuführen. Nach Abschluss des Kurses stehen viele Türen für Dich offen und Du hast die Möglichkeit, Dich in unterschiedliche Richtungen weiter fortzubilden. Wenn Du allerdings schon einmal programmiert hast und ein bisschen mit Daten umgehen kannst, bist du eigentlich schon bereit für einen der nächsten Kurse.

The introductory course is open to all. However, the course is in German and you must be fluent in German to take part since the course is very interactive, including a lot of team communication. You will be picked up where you are and accompanied until you are able to do your own project. After completion of the course many doors will be open for you and you will have the opportunity to continue your education in different directions. However, if you have already done some programming and know a little bit about data, you are already ready for one of the next courses.

Machine Learning with Tensorflow

Machine Learning with Tensorflow

This course will give you an overview of neural network and their applications in different fields e.g. working with images, texts, or time series. It is a very hands-on approach and you will get a lot of working examples within the course. It is a perfect start for your project. This course will not stress the underlying principles of machine/deep learning but focus on the application.

As an example of a good fit for the course, You already have some programming knowledge and are interested in getting hands-on knowledge in how to train and use machine learning algorithms.

Deep Learning

Deep Learning

The course Deep Learning from Scratch will give you an overview of the basic principles behind machine learning. How and why they work, and you will write your own code in python and implement deep learning algorithms from scratch, thus reaching a deeper understanding of how things work and a solid knowledge for your further projects. Since this is a rather technical course you are required to have done some intermediate programming and also know about matrix algebra. You can still learn this along the course but it will take you a lot more time to keep up with the course.

For the Advanced Deep Learning course you should have already completed the Deep Learning from Scratch course or have a comparable level of knowledge.

As an example of a good fit for the Deep Learning from Scratch course, You have knowledge about programming and linear algebra (working with vectors and matrices) and are interested in getting in-depth knowledge on how to implement machine learning algorithms.

Special Issue Courses

Special Issue Courses

Each semester, we ususally have different special issue courses on topics like natural language processing or generative adversarial networks.

For these courses you typically should already have a basic understanding of machine learning. However, please check the course descriptions and also do not hesitate to contact the course guide for any questions. In general these courses are a great opportunity to connect with others that are interested in the same issues as you.

As an example of a good fit for the course, You already have some knowledge about Machine Learning (ideally you followed one of our previous courses) and are interested in learning more about the particular field of Machine Learning.