The most valuable contributors to machine learning are often generalists. Especially in 2017, there is a lot of hype around particular machine learning methods. Candidates who have learned how to use a certain deep learning package in an online course and are applying to jobs remind me of people in the 1990s, when there was similar hype around the web, who read the "Learn VBScript in 20 Days" kinds of books instead of learning the fundamentals of computer science.
The skills that have remained important are (a) understanding the fundamentals of statistics, optimization, and building quantitative models and (b) understanding how models and data analysis actually apply to products and businesses.
In addition to that, the following is relevant to high impact roles in 2017:
Knowing how to write high quality software - the days of one team writing throwaway models and another team implementing them in production are slowly coming to an end. With programming languages like Python and R and their packages making it easy to work with data and models, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design.
Working with large data sets. While "big data" is a term used way too often, it is true that the cost of data storage is on a dramatic downward trend. This means that there are more and more data sets from different domains to work with and apply models to.
And yes, knowing something about at least one of the popular areas of the field that have gotten traction lately - deep learning for computer vision and perception, recommendation engines, NLP - would be a great thing once you have the fundamental understanding and technical proficiency.