The ML/DNN rabbit-hole goes deep. If the video above leaves you wanting more, http://www.deeplearningbook.org/ does a good job on drilling into more specifics for the various techniques used. The examples on the tensorflow webpage are also very good.
Edit: I should mention that the class mainly focuses on neural networks and image recognition. However, once you have the foundation, you can apply your skillset to a vast range of applications.
Definitely recommend that as a good starting point. Isbell and Littman can be a bit cheesy at points, but they're very clear and thorough.
Don't worry that just because it isn't using deep nets that it isn't state of the art or won't get the job done well. That would be like thinking python's built-in sort function isn't sufficient because it doesn't use Spark.
Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data. The course focuses particularly on computer vision and language modelling, which are perhaps two of the most recognizable and impressive applications of the deep learning theory.
I have a couple of applications in mind, mostly time series predictions. But the machine learning field seems to be vast and I don't know where to start.