Machine learning is permeating many fields of work. As a new ‘system technology’¹, its impact on organizations and society is expected to be of the same magnitude as that of the steam engine or electricity. As such, more and more professionals are seeking to acquire the necessary understanding and skills to apply machine learning in their day-to-day work. Hence more people without a background in either computer science or statistics - let alone both - have a need for high-quality, open access content to explore and learn data science by themselves.

Now there is a lot of machine learning learning materials out there, so why this anthology? Based on my experience in teaching professional education course on data & AI, I am continouly challenged to:

• curate content for different professional learning paths, combining various existing open access materials that can be readily shared and thus contribute to the democratization of know-how in this field of work;
• finding a balance between too technical vs. too vague, handwaving or even downright wrong;
• take a hands-on, problem-based approach. Rather than, say, explaining the principles that underlie regularization, we choose to demonstrate these principles using the simplest algorithms. With a little math, everyone should be able to understand how LASSO performs regularisation for regression models. With this intuitive understanding, you can move on to more complex algorithms and applications, and reason where and how to use regularisation.

All the work in this GitHub organization is licensed under CC BY-SA 4.0