From an application perspective, it's more important to understand how the overall ML process work, the key concepts, and how things are fitted together. Deep

From an application perspective, it's more important to understand how the overall ML process work, the key concepts, and how things are fitted together. Deep Learning is a part of that. Lots of these are already wrapped in libraries and API, so it's a matter of preparing the correct data, calling the right API's, and utilizing the result.