The latest thinking around 'data science as a product' seems to take this approach. See Building Machine Learning Powered Applications for one resource https://www.oreilly.com/library/view/building-machine-learni...

A lot of the resources proposed in the comments focus on theoretical knowledge, or a particular sub-domain (Reinforcement Learning, or Deep Learning). I recommend a top down approach where you pick a project and learn by building it. This can be easier said than done however, and after mentoring dozens of junior Data Scientists I wrote a how-to guide for people interested in using ML for practical topics.

You can find it from O'Reilly here (http://shop.oreilly.com/product/0636920215912.do) or on Amazon here (https://www.amazon.com/Building-Machine-Learning-Powered-App...).