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The goal of a Masters degree in Machine Learning is ideally to prepare you for core research and engineering roles where you work on improving, adapting and inventing new algorithms and techniques.

However, such roles are very limited in the industry. Most jobs you will find expect you to be able to apply, combine and optimize existing algorithms to given real-world problems. This requires a different set of skills that are seldom taught at University (you're typically expected to pick up these practical abilities on your own).

Udacity's Machine Learning Engineer Nanodegree, like its other programs, is heavily project-based, and has been developed with feedback from industry partners in order to emphasize the skills and concepts that are most relevant for the vast majority of jobs that are out there. This focused curriculum allows people with limited time or a related background to efficiently get started in machine learning.

Keeping this mind, ask yourself what your ultimate goal is, what time constraints you have, and choose accordingly. There is no shortcut to success, esp. in a competitive and highly technical field like machine learning - whether you opt for a Masters degree or a Nanodegree, you will have to spend considerable effort building a strong public profile (e.g. by participating in Kaggle competitions, and working on additional projects) in order to make yourself stand out from the crowd.

Good luck!

Disclaimer: I work at Udacity, in case you didn't realize by now :)

Which companies are hiring the ML nano-degree graduates?

Several companies have hired MLND grads, including Google, PwC, Cyient Insights, Henry Ford Health System, TransVoyant, AirBnB and Udacity itself. Also, it is common for companies to hire candidates internally once they demonstrate they've acquired the relevant skills.

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