Machine learning

Machine learning with Python: excellent introduction to basic machine learning theory (regression and classification) with applications in Python.
Machine learning and data science in Python: hands-on tutorial, the course introduce useful libraries for data science (numpy, pandas, matplotlib, seaborn) and basic machine learning (scikit-learn) through applicative case-studies.
Introduction of PyTorch for deep learning: a first introduction about the usage of the popular PyTorch framework for deep learning.

Deep learning specialization: a set of theoretical/practical cources for mastering your deep learning knowledge. Depending on your project and interests, specific sub-courses may be taken.
Deep Learning with Python and PyTorch: in this course, you will learn how to build deep neural networks in PyTorch.

Machine Learning - University of Amsterdam: a bachelor level theoretical course on machine learning and deep learning.
Machine Learning 1 - University of Amsterdam: advanced master level theoretical course on machine learning and deep learning.
Deep Learning - University of Amsterdam: advanced master level specialization course on deep learning. Practical lectures are included.
Stanford University - Convolutional Neural Networks for Visual Recognition: highly recommended course on CNN. Students interested in computer vision with deep learning are highly encouraged to take this course. The first lectures may be useful for people interested in learning deep learning concepts.

RWTH Aachen University: Mathematical Optimization for Engineers: mathematical optimization course from Professor A.Mitsos (RWTH Aachen). The course provide a comprehensive theoretical introduction to optimization with a focus on chemical and energy engineering applications.

To get started with Convolutional Neural Networks (CNN), we recommend the following lecture series from Stanford University.