There is a wide variety of courses offered on the Internet providing you with knowledge about Artificial Intelligence (AI) and Machine Learning, due to the number of available courses however, it is sometimes very difficult to find the best ones.
In our opinion the courses from Andrew Ng and his current project are outstanding though, which is why we use these courses as one of the pillars in teaching AI at
The courses are sorted according to complexity and needed prior knowledge. Starting with a course providing a conceptual understanding of AI and finishing with courses providing a fundamental theoretical understanding for its implementation.

You can select from four different types of courses:
Self-Paced Online Courses - These courses are fully online and you can enrol and complete the courses whenever you want.
Group-Based Online Courses - These courses are online courses conducted in learning groups. You will meet regularly at the starterkitchen from opencmapus and discuss questions or problems from the past course material. The courses start as soon as enough persons registered.
Offline Courses - These courses are traditional offline courses, in which most the courses content is taught in class. The courses start parallel to the university lectures.
Blended-Learning Offline Courses - In these courses the content is taught online, while the time in class is used for practical exercises and clarification of classes. The courses start parallel to the university lectures.

AI for Everyone (Self-Paced Online Course or Group-Based Online Course)

Free of charge* | Approx. 9 study hours to complete

“AI for Everyone” is a non-technical course that will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today’s AI can – and cannot – do. Finally, you will understand how AI is impacting society and how to navigate through this technological change.

If you are a non-technical business professional, “AI for Everyone” will help you understand how to build a sustainable AI strategy. If you are a machine learning engineer or data scientist, this is the course to ask your manager, VP or CEO to take if you want them to understand what you can (and cannot!) do.


Click here to enroll in the self-paced online course now!

Einführung in Data Science und maschinelles Lernen (Offline Kurs)

Ohne Gebühr | 1 Semester (etwa 75 Lernstunden)

Du bekommst eine praktische Einführung in Data Science und maschinelles Lernen (ML) auf Basis der Programmiersprache "R". Die Programmiersprache und die Entwicklungsumgebung RStudio werden von Grund an erklärt. Falls Du bisher noch gar keine Programmiererfahrung hast, wird der Fortschritt insbesondere anfangs sehr schnell für Dich sein, Du solltest daher einplanen, dass Du gegebenfalls zusätzlich Zeit benötigen wirst, um Dein Verständnis über Online-Ressourcen zu vertiefen, die wir im Rahmen des Kurses zur Verfügung stellen.

Nach der Einführung in die Grundlagen von R wirst Du im Team an einem Auswertungsprojekt arbeiten, im Rahmen dessen Du die verschiedenen vorgestellten Verfahren praktisch umsetzt.


Klicke hier, um Dich jetzt zu bewerben!

Machine Learning With TensorFlow (Blended-Learning Offline Course)

Free of Charge | 1 Semester (approx. 150 study hours)

This course is based on's course "TensorFlow in Practice". It will provide you with an introduction to machine learning and deep learning, teach you about computer vision using convolutional neural networks, natural language processing, and how to solve time series and forecasting problems (see also corresponding course description below). Guided by weekly meetings and discussions, you will then apply the newly aquired knowledge to implement your own machine learning project in a team.

You should be comfortable coding in Python or a similar programming language and understand high school-level math. Prior machine learning or deep learning knowledge is helpful but not required.

Click here to apply now!

Tensorflow in Practice (Self-Paced Online Course)

Free of charge* | Approx. 1 month to complete (suggested 13 study hours/week)

This course consists of four individual course units and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In the courses you will:

  • Learn how to build machine learning models in TensorFlow
  • Build image recognition algorithms with deep neural networks and convolutional neural networks
  • Build natural language processing systems using TensorFlow
  • Learn how to benefit from existing neural nets using transfer learning
  • Build time series models in TensorFlow using RNNs and 1D ConvNets

You should be comfortable coding in Python or a similar programming language and understand high school-level math. Prior machine learning or deep learning knowledge is helpful but not required.

Click here to enroll now!

Deep Learning (Self-Paced Online Course)

Free of charge* | Approx. 3 month to complete (suggested 11 study hours/week)

This course consists of five individual course units, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

You should be comfortable coding in Python or a similar programming language.

Also, you already should have some experience in the foundations of machine learning, which is why we recommend taking weeks 1-3 of Andrew Ng's classic Machine Learning course first if you are in doubt.

Further, linear algebra is key to understand the core of machine learning and deep learning. Luckily Andrew Ng's Machine Learning course also includes a refresher on linear algebra. If you need more than that, we recommend you to take this Linear Algebra course from the Mathematics for Machine Learning course program from Imperial College London.

Click here to enroll now!

* In order to enroll for free into a Coursera course, you have to register with Coursera first and then click on the 'Enroll for Free' button. On the popup showing next you simply click on the blue 'Audit' link in the bottom left corner.
In order to do the above mentioned Coursera Specializations 'TensorFlow in Practice' or 'Deep Learning' for free, you do not sign-up directly to the Specialization but for each of the four or five respective courses. To do so you simply scroll down on the entry page of the Specilization you want to do; there you will find the list of respective courses.