Online Master’s in Machine Learning: Why It Might Be Right for You

Machine learning plays a crucial role in our daily lives. With every package that’s delivered, every notification we get on our phones, and every post we see on social media, we’re interacting with ML algorithms and models. In the decades to come, our encounters with machine learning will only increase in frequency.

With so much focus on artificial intelligence at the heart of machine learning, it’s easy to lose sight of the fact that these technologies are powered not just by computers, but by real people. The demand for machine learning engineers has never been greater, with qualified professionals earning an average annual salary of $124,059 according to Indeed — far higher than the average annual salary in the US, which the U.S. Bureau of Labor Statistics pegged at $58,260 in 2021.

Unsurprisingly given the favorable job market and high compensation, master’s degrees in machine learning are seeing accelerating demand, with universities heeding the call. Stanford’s AI Index notes that the 73 respondents to its global survey of leading universities reported increasing graduate offerings teaching practical artificial intelligence and machine learning skills by 40% over the four academic years leading up to 2020, with faculty specializing in AI & ML growing by almost 60% over the same timeframe.

Alongside expanded AI & ML departments and curricula, master’s-level ML instruction is increasingly being offered online, allowing students the opportunity to study at the world’s leading universities while retaining flexibility and affordability. Is an online machine learning master’s right for you? In this article, we’ll cover the basics of the degree before diving into the pros and cons of online study and previewing some great programs to consider.

What’s a master’s in machine learning?

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.

But a master’s in machine learning won’t always be packaged as such. Take, for instance, Carnegie Mellon’s Master’s in Machine Learning, Georgia Tech’s MS in Computer Science with ML Specialization, and Drexel University’s MS in Machine Learning and Artificial Intelligence. These differ in the precise major, but all provide crucial background knowledge and the fundamental training necessary to design and implement machine learning models through courses focusing on:

• Programming

• Statistics & linear algebra

• Advanced algorithms

• Reinforcement learning

• Deep learning & neural networks

• Data mining 

• Natural language processing

• Computer vision

• Etc.

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

As you can see, a machine learning master’s curriculum covers extremely specialized STEM topics. In general, applicants to machine learning master’s programs are expected to have earned their bachelor’s degrees in computer science, mathematics, or another technical field. 

There are certainly instances of industrious students breaking into machine learning with a bachelor’s in the social sciences or the humanities, or even experienced professionals in other fields transitioning to machine learning later in their careers. But to gain admission to a master’s program these individuals will need to demonstrate skills in computer science and advanced mathematics or take introductory courses before they begin their master's degree — or both. 

How is an online master’s in machine learning different from an in-person degree?

You’ve got a good idea of what a master’s in machine learning entails, who these programs are looking to admit, and what students will learn once they matriculate — but what changes once things move online?

Curriculum

In terms of the learning itself, the answer is not much. So much of the machine learning curriculum is digitally native that moving things online isn’t such a shock. Some schools, like Drexel, give their students the option to take their master’s program online or in person, while schools like Georgia Tech and Columbia University offer online-only master’s programs in computer science that allow students to concentrate on machine learning. Regardless of the modality, students receive equivalent learning content, with courses in deep learning and natural language processing and opportunities for independent projects and even internships.

Campus Experience

In general, the flexibility of online study will come at the cost of a robust campus experience, so before you commit you’ll want to make sure you’re okay with foregoing the ivy-covered walls of Columbia University or Stanford University’s Frederick Law Olmsted-designed grounds for your bedroom, living room, or kitchen.

As we mentioned above, this certainly has its advantages if you want to stay close to home, perhaps to care for a family member, but it also means that you’ll miss out on the great things you can find on campus: cultural happenings, parties, and academic resources like specialized computer science libraries.

Networking

Another thing that gets more difficult — but not impossible — with online coursework is networking. The relationships you make in graduate school can offer an important support system during your program and the keys to new opportunities down the road, so it’s crucial to embrace your inner social butterfly and spread your wings.

This might initially seem difficult in an online setting until you remember how social media has revolutionized how we connect with people around the world. It might not be tossing a frisbee in the quad or hitting a toga party, but whether through message boards hosted by the university or through student-led communities on Slack, Facebook, Twitter, or Reddit, there are plenty of ways to get yourself out there, learn from others, and build long-lasting relationships.

Internships

As with networking, internships can require some imagination in an online setting. Traditionally, universities have partnered with companies in their proximity to offer internship opportunities, such as how Drexel University might partner with tech-focused businesses in Philadelphia. 

Again, studying machine learning or another computer science online means that most of your work, both at school and in industry, will take place in digital environments. While you might not benefit from local connections, there are plenty of opportunities to intern remotely. A program might also help you find opportunities right in your hometown.

Flexibility

One of the biggest draws of online study is the flexibility it allows learners. Because many courses can be taken asynchronously, watching video lectures and completing readings and exercises on your own time, you can fit in your graduate studies around your job, your family obligations, your athletic regimen, or your busy cosplay schedule.

The benefit here is not just convenience: being able to keep working or caring for a loved one while you study has a real financial upshot. Even if — as we’ll discuss next — online study is more affordable, being able to keep income coming in and reduce other costs can make graduate study far more feasible, especially with a lucrative salary on the horizon.

Cost

Let’s face it: higher education is expensive. Spurred by breakneck growth in university administrations and college campuses, tuitions are higher than they’ve ever been and only growing. The average cost of one year of graduate study in 2018 — and most master’s degrees in artificial intelligence require two — was $12,171 for public institutions (in-state) and $27,776 for non-profit private institutions. 

Only structural changes will provide relief for those seeking undergraduate and graduate degrees, but online study can offer a substantial improvement when it comes to the total cost of education. As we’ve mentioned above, not having to relocate, take time off from your job, or hire a caretaker for your loved ones — not to mention transportation costs — already puts a significant amount of money back in your pocket. 

It gets even better. As U.S. News & World Report notes, “the average per credit price for online programs at the 168 private colleges that reported this information is $488 – lower than the average tuition price for on-campus programs at ranked private colleges, which is $1,240 among the 113 colleges that reported this information.” 

It’s worth keeping in mind that some disagree that online education offers a cost-benefit compared to in-person study — your best bet will always be to crunch the numbers yourself — but if you’re sitting on your couch trying to decide between studying online and heading to campus, keep in mind the potential financial upshot of staying right where you are.

While the online machine learning master’s degree is still a relatively new offering for universities, there are already some great options. Here are a few:

Though they lack specific focus on machine learning, other great schools — including Cornell University, UC Berkeley, Stanford University, and Penn State — offer online master’s programs in artificial intelligence. If you’re interested in online graduate programs in artificial intelligence, check out our recommendations page.

How do you pick a masters in machine learning online program that’s right for you?

We’ve just explored a few great online machine learning master’s programs — but how do you know which of these, or the many other programs out there, will be right for you? Here are some things to keep in mind as you’re making your decision:

Admissions Requirements & Prerequisites

Before you fall in love with a program and start assembling your application, it’s crucial to ascertain whether your application will be welcome. When schools list requirements and prerequisites for study, they mean it. Their top priority is turning out stellar machine learning professionals who will make an impact, whether in research or in industry. Accordingly, they will only accept students who show an aptitude to thrive in their program.

So, check whether a school you're interested in requires a certain GPA or certain scores on the  Graduate Record Examination (GRE) or other standardized tests. If you’re right on the edge, there’s no harm in shooting off an email to the admissions director before you spend the time crafting an application.

Curriculum Specifics

Though similar, not all master’s programs are created equal: curricula will differ based on the strengths of the faculty, the department’s vision for the program, and even a school’s location and the nearby industry connections it can cultivate.

When researching programs, you don’t need to already have an idea of where exactly in machine learning you want to end up. It’s useful, however, to be self-aware if you have an idea of where you want to end up. If you don’t, that’s great — you’ll fit in almost anywhere. But if you have a particular interest — natural language processing, say, or working with big data — you will want to make sure that a program can support you in it.

Student Outcomes

Before even applying to a program, you want to be sure it will get you where you want to go — there’s no use spending time and money on education if you won’t recoup this investment with a great career in ML. Oftentimes, departments will proudly list graduate placement on their websites. You might also hit up LinkedIn to see where alumni have ended up.

Cost

Last, but certainly not least, you want to be realistic about how much a program will cost in the short term, the long-term financial benefit of graduating from the program, and how these square with your financial reality. 

As you consider this, you’ll have to take into account the cost of the program and balance it against both the value of a program’s curriculum and the value of a university’s name on your resume. On paper, “Columbia University” seems like it would open more doors than “Georgia Tech” — but does that really justify paying ten times as much for your degree? Only you can decide this, and it’s important you give it some thought.

What are the next steps?

It’s already a big decision to choose a program (or programs) to apply to, and that decision isn’t made any easier when you also are deciding between studying in-person or online. Ultimately, the decision will be yours: you’re the expert on your current situation and your goals for the future.

Once you make a decision, we’ll be here to help you with the next steps: you can head over to our applications page for the latest tips on how to make your application stand out from the rest of the pile.

Alternatively, if you’re still on the fence about going into machine learning, you can find more information on detailed machine learning engineer responsibilities, qualifications, and salaries at our Careers portal to help you come to a decision.