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

Published on: Nov 6, 2022
Last Updated on: Apr 1, 2023
By: Editorial Staff
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Google “machine learning masters” and you’ll find a flood of online and in-person programs offering intensive training in applied mathematics, programming, and machine learning techniques, promising you an illustrious and well-compensated career as a machine learning engineer or data scientist.

Such offers are certainly tempting, and they seem borne out by the data. The machine learning job market continues to be hot, with LinkedIn ranking machine learning engineer fourth on its 2022 Jobs on the Rise list with a salary range of $72,600–$170,000. Even at the bottom of this range — and most machine learning engineer salaries are likely closer to Salary.com’s average of $120,883 — a qualified machine learning professional is earning well above the average annual salary in the country, which the U.S. Bureau of Labor Statistics pegged at $58,260 in 2021.

But is a master’s in machine learning really worth it? In other words, does earning a master’s degree really set you apart from the crowd of other job applicants to justify the considerable expense, especially considering alternative options like self-study or the new machine learning bootcamps popping up seemingly every day? In this article we’ll tackle this question, giving you the facts plus some tips so that you can make the determination for yourself.

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 learn to maximize a numerical award 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:

  • Applied mathematics, including probability, statistics, and linear algebra

  • Programming with a programming language like R, Python, or Java, as well as software libraries like PyTorch, TensorFlow, and Pandas.

  • Machine learning methods, including supervised learning, unsupervised learning, reinforcement learning, and deep learning using an artificial neural network

  • Key artificial intelligence subdisciplines like natural language processing, computer vision, and robotic process automation

  • Data skills, including data collection and preparation, data mining, data analysis, data visualization, and data architecture

You can find detailed explanations of each of these in our primer on machine learning skills.

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 2 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.


Online master’s in ML programs allow students to access live and pre-recorded learning content from the comfort of their own homes. Interaction with peers is often facilitated through digital touchpoints like exclusive Slack channels. Some programs only offer full-time or part-time study, while for others, students have the flexibility to pace their learning as they see fit.

Hybrid Online & In-Person

Hybrid master’s programs take place primarily online, though students have the option to take certain courses on campus or to travel to campus for summits with their professors and colleagues. This option allows students to retain flexibility while creating opportunities for face-to-face networking.


The most traditional path, an in-person master’s degree program requires students to remain in residence for the duration of their study, either on campus or nearby so that they can regularly commute to class.

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 associated costs with machine learning master’s degrees?


Higher education is expensive. In part due to breakneck growth in university administrations and college campuses, tuitions are higher than ever and only growing. The average cost of one year of graduate study in 2018 — and most master’s degrees in machine learning require two — was $12,171 for public institutions (in-state) and $27,776 for non-profit private institutions. At elite institutions, tuition runs much higher. Pursuing a machine learning master’s degree at Carnegie Mellon University, for example, would cost $52,000 per year before housing and fees. 

Relocation, travel, and/or housing costs

Studying in person, even if only temporarily, means that you’ll have relocation, travel, and housing costs. Especially in larger, more popular cities, housing can easily cost you $1000 per month or more. When researching programs, make sure to look up cost of living expenses and factor these into your calculations.

Lost potential income and/or other expenses

If you are considering a master’s degree and currently working or a primary caregiver, keep in mind that learning full-time will mean that you won’t be pulling a salary and/or might need to pay for a babysitter or other caregiver. 


Few have the cash on hand to pay for master’s-level study out of pocket, and so are forced to take out loans. You should only seek private loans after you exhaust other financial aid options such as private scholarships and grants and federal grants, scholarships, loans, and work-study programs. If you consider doing so, make sure to include the interest you would expect to pay in your calculations.

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.

Machine learning engineer

An ML engineer works on the end-to-end production of machine learning models — designing, developing, and deploying them — as well as maintenance after deployment.
According to Salary.com, the average machine learning engineer in the US earns $120,883 annually.

Data scientist

A data scientist designs, develops, and executes new ways to turn raw data into business insights and solutions using machine learning and other data analytics.

According to Salary.com, the average data scientist in the US earns $139,202 annually.

Data engineer

A data engineer focuses on building, streamlining, and maintaining the data pipelines that are central to data science, machine learning, and other data analytics.
According to Salary.com, the average data scientist in the US earns $112,896 annually.

Software engineer

A software engineer specializing in artificial intelligence or machine learning system design focuses on building and maintaining a particular product (an app, API, or service) that brings AI capabilities to consumers and businesses alike.
According to Salary.com, the average software engineer in the US earns $122,400 annually.

Someone holding a master’s degree would likely be a competitive candidate for any of these positions, depending, of course, on individual strengths and knowledge areas. Crucially, however, a master’s degree also opens the door for positions farther along on the career path that require candidates hold master’s degrees to apply. These senior positions often offer tens of thousands of dollars more in compensation.

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:

  • 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?

  • How likely am I to succeed in such a program?

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

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

  • 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?

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