Is a Master’s in Machine Learning Worth It? What You Need to Know to Make Your Decision

What is machine learning?

Before we get started, let’s clear up an important question: what actually is machine learning?

The practice of machine learning — a subdiscipline of artificial intelligence — is the development of computer programs that can progressively improve their capabilities as they continually process data, eventually arriving at a model that can engage in complex analysis of big data sets, make accurate predictions, or perform automated tasks.

Central to this process are machine learning algorithms, bits of computer code that process input data and produce usable output data. These algorithms are trained by being fed “training data,” relevant input data for which the output is already known (supervised learning); written to train themselves to find patterns and other signals in unlabeled data sets (unsupervised learning); or written to learn to maximize a numerical reward aligned with the desired action or other output (reinforcement learning).

What does a master’s in machine learning entail?

A master’s in machine learning is a graduate degree awarded upon completion of an academic program offering both instruction in the core topics of machine learning like deep learning, reinforcement learning, and big data analytics and the skills needed to put expertise in these areas into practice.

Though many schools offer degrees with the name “master’s in machine learning,” the curriculum can be variously designated. Some schools, like Georgia Tech, offer a master’s in computer science with a machine learning specialization, while others, like Drexel University, offer a combined master’s in artificial intelligence and machine learning.

Despite the differences in these programs’ designations, master’s students can generally expect to cover the following. You can find detailed explanations of each of these in our primer on machine learning skills.

1. 1.

   Applied mathematics

   Programs commonly include probability, statistics, linear algebra, and other quantitative foundations.

2. 2.

   Programming

   Students may work with languages like R, Python, or Java, as well as software libraries such as PyTorch, TensorFlow, and Pandas.

3. 3.

   Machine learning methods

   Coursework often covers supervised learning, unsupervised learning, reinforcement learning, and deep learning using artificial neural networks.

4. 4.

   AI subdisciplines

   Relevant topics may include natural language processing, computer vision, and robotic process automation.

5. 5.

   Data skills

   Programs often emphasize data collection, preparation, mining, analysis, visualization, and architecture.

Who’s a master’s in machine learning for?

Applicants to machine learning master’s programs have typically earned their bachelor’s degrees in computer science, applied mathematics, or some other STEM field.

Often, applicants for graduate admission have already been working as a software engineer, data analyst, or data scientist, but want to gain skills, expertise, and credentials that will make them attractive candidates for more senior positions, many of which prefer applicants that have master’s degrees.

Industrious students with a bachelor’s in the social sciences or the humanities are certainly able to break into machine learning, as are experienced professionals in other fields transitioning to machine learning later in their careers. But to gain admission to a master’s program, individuals from “non-traditional” backgrounds need to demonstrate skill in computer science and advanced mathematics or take introductory courses before they begin their master’s degree — or both.

What are the different ways you can pursue a machine learning master’s degree?

With full-time study, most master’s programs last around two years. In keeping with the growing popularity of online study, schools are increasingly offering part-time or flexible options that can be completed partially or fully remotely.

Is a machine learning master’s degree worth it for you?

In determining whether a machine learning master’s degree makes sense for you, take into consideration the associated costs as well as the potential outcomes and their likelihoods.

What are the potential outcomes of a master’s in machine learning?

In some cases, students who graduate with their master’s in machine learning choose to remain in academia and pursue a doctorate. You can learn more about PhD programs in artificial intelligence and machine learning through our guide.

For the most part, however, graduates of ML master’s programs will go on to become machine learning engineers, data scientists, data engineers, or software engineers — all of which offer lucrative starting salaries.

How likely are these potential outcomes?

At the moment, it’s tough to say. The machine learning and data science job markets are certainly booming, and while it’s probable, it’s not yet clear if master’s programs in machine learning are consistently placing graduates in jobs any better than bachelor’s programs, bootcamps, or master’s programs in other disciplines — in part because these programs are so new and in part because colleges and universities aren’t terribly transparent on student outcome.

The likelihood of an outcome that would justify the expense of a machine learning master’s degree is also difficult to measure because things like the quality of a program — the level of its instruction, its reputation, its industry connections — and the quality of individual candidates — how much they’ve retained from what they learned, how successfully they can demonstrate their skills through a portfolio of machine learning projects, how well they interview — are difficult to quantify.

So what can you do? Become a guerilla researcher: identify graduates of programs you are interested in on LinkedIn or other social media, see where they ended up, and compare their paths and qualities to yours. If possible, get in contact and see if they’d be willing to chat about their experiences. While you won’t walk away with hard data, you’ll certainly get a better idea of the quality of the program and where you stand. You might also be thankful for the professional networking you are building if and when it comes time to graduate from your program and find a job.

A master’s in machine learning might be for you, but what if it isn’t?

Ultimately, the decision of whether a master’s in machine learning is worth it is up to you. As you decide, ask yourself questions like the following:

1. 1.

   Peer outcomes

   Are there individuals of similar background, aptitude, and achievement who have leveraged a master’s in machine learning to land a lucrative position? Are these individuals exceptions, or does this happen frequently?

2. 2.

   Program fit

   How likely am I to succeed in such a program?

3. 3.

   Flexible options

   Are there intriguing online options, or are there local options that would allow me to cut down on housing or other costs of living?

4. 4.

   Part-time options

   Are there intriguing part-time options that would allow me to continue working or taking care of a loved one?

5. 5.

   Loan risk

   Would I need to take out private loans? Am I confident that I would get a job with high enough compensation that I’d be able to pay them off quickly?

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Conclusion & Next Steps

If you decide to pursue a master’s in machine learning, congratulations. You can find our favorites over in our machine learning master’s degree guide. We also have a guide specific to online master’s programs.

If you decide that an ML master’s doesn’t make sense for you, but you’re still interested in machine learning, you might consider a machine learning bootcamp or certificate program instead. You might also consider options in data science, like a data science bootcamp or a data science master’s program. You can learn more about the difference between data science and machine learning through our clarifier.