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Machine Learning
Ensuring Success Starting a Career in Machine Learning (ML)
Machine learning (ML) careers in industry and academia are in such high demand, how do you assure you can succeed in such a competitive field?
Machine learning careers are being sought out by many, from researchers, industry experts, to machine learning enthusiasts. Everyone is trying to get their feet wet working with machine learning to contribute to such a rapidly moving field.
With massive open online courses (MOOCs) offering machine learning paths from Coursera, Udacity, edX, and others. To leader institutions in academic research such as Carnegie Mellon, Berkeley, MIT, Georgia Tech, and others.
How do you know what’s the right path to follow with so many options?
It depends. It is best if you weight what is essential for you to pursue in terms of your career in machine learning. Below, please find some of the main differences between pursuing machine learning coursework with an MOOC or with a university.
Differences between MOOCs and Academia
MOOCs tend to be more lenient and not as rigorous as academia. They are also less time-consuming and are best fit for busy individuals trying to learn something new while working fulltime on their jobs and/or taking care of their families.
Pursuing a machine learning career on the academic end will be more time consuming, more rigorous, and will ask you to give out your best, day in and day out, throughout the duration of the program.
MOOCs can help you get your foot in the door, especially if you already have a background in computer science, statistics, mathematics, or another STEM-related field. Yet, academic machine learning programs work with state-of-the-art research and projects, which will not only help you get your foot in the door as a machine learner but will give you the required expertise to be a leader on the field as soon as you finish the program.
However, it all depends on how passionate you are about ML. There are amazing people, whos’…
