How long would it take to become proficient in machine learning for someone with a non-statistical mathematical background? I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was completely deterministic. I took several probability/statistics and one machine learning module 5-6 years ago during my BSc, and a stochastic ODE module during my MSc but that's about it..its been all applied mathematics since then.

I am considering leaving academia and entering industry and it seems like there are far more jobs in the area of data science/machine learning than there are for my skillset.


If I left academia and began 'studying up', how long do you think it could take me to gain the skills required for a data science/machine learning position in industry?
It seems like there is a very wide variety of science/machine learning techniques and obviously there isn't time to learn all or even most of them. So what approaches are absolutely essential for data science/machine learning in industry these days and what is the most efficient route to gaining these skills?
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required. 

One option is :


Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
Start applying the knowledge in real world problems as soon as possible. 
Learn various types of neural networks (deeplearning.ai is one place to start)
Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
Get an internship 


This will take 5 - 6 months.