Machine learning has established itself as anirreplaceable tool in modern day decision making, and the rise of quantum computing is likely to push the capability of machine learning to new heights.
This course will take you through key concepts in quantum machine learning, such as parameterized quantum circuits, training circuits, and applying them to basic problems. It is based on an interactive textbook and finishes with a project that you can use to showcase what you've learnt.

It is a condensed introduction to quantum machine learning for autonomous self-learners. 

• Parameterized quantum circuits;
• Data encoding;
• Variational classification;
• Quantum feature maps and kernels; 
• Quantum generative adversarial networks 

By the end of the course, you‘ll be able to... 

• understand the main concepts in this field;
• design supervised and unsupervised learning models; 
• implement your own quantum machine learning project.

• Undergraduate mathematics like linear algebra and calculus;
• Basics in Python programming; 
• Fundamentals in quantum computing