Have you been over all of the introductory machine learning tutorials out there? Have you read all the algorithm theory you can handle? But still don't have any idea how to design a real world machine learning system? Not sure what kind of software architecture is useful? And even if you did, would you still have virtually no idea how to deploy and maintain it afterwards?

As the name implies, this course takes a more applied perspective than Andrew Ng's machine learning lecture at Stanford. You will see more code than mathematics. Concepts and algorithms are using the popular Python libraries scikit-learn and Keras.

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length.