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 introduces 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 courses 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.

RWTH Aachen University: Mathematical Optimization for Engineers: mathematical optimization course from Professor A. Mitsos (RWTH Aachen). The course provides 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.