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’ backgrounds are in STEM fields and only pursued MOOCs, and they are doing terrific work in the AI industry.
Something else to consider is, are you genuinely passionate about tinkering with data? Do you see yourself working with machine learning models for decision making? — if the answer is yes, then please, go right ahead, follow your dreams to become a machine learner.
For instance, on the academic end, Carnegie Mellon provides a self-assessment test that can give you an idea about the expected background for incoming students to their machine learning masters program. Such mentions that various types of math are needed, such as multivariate calculus, linear algebra, elementary probability, and statistics to at least an undergraduate level.
Their website also mentions that incoming students must have a strong background in computer science, along with a solid understanding of complexity theory and good programming skills.
However, if you don’t have such a background and you’d like to pursue a machine learning program with a university, please don’t be discouraged from applying, as most elite universities take a “holistic approach” when it comes to admitting students — with an emphasis on the student as a whole, and not just select pieces of information.
If you are genuinely fascinated by the scientific field of machine learning, it is up to you to determine the path that would benefit your career the most and fit your needs. Especially now, while machine learning salaries continue to rise through the roof.
Thank you kindly for reading. Your feedback is always welcome.
DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University, nor other companies (directly or indirectly) associated with the author(s). These writings do not intend to be final products, yet rather a reflection of current thinking, along with being a catalyst for discussion and improvement.
Academic Paths to Machine Learning:
Best Masters Programs in Machine Learning (ML) for 2020
Best machine learning masters programs in the United States.
Best Ph.D. Programs in Machine Learning (ML) for 2020
Best universities to pursue a Ph.D. in machine learning in the United States.
AI for Everyone | Andrew Ng | Coursera | https://www.coursera.org/learn/ai-for-everyone
Machine Learning Crash Course | Google | https://developers.google.com/machine-learning/crash-course/
Intro to Machine Learning | Udacity | https://www.udacity.com/course/intro-to-machine-learning--ud120
Machine Learning Training | Amazon Web Services | https://aws.amazon.com/training/learning-paths/machine-learning/
Introduction to Machine Learning | Coursera | https://www.coursera.org/learn/machine-learning
Machine Learning | Tom Mitchell | McGraw Hill, 1997 | Carnegie Mellon University | http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html
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