The recent AI boom has left a lot of students eager to specialize in artificial intelligence and machine learning. If you’ve seen what this technology can do first-hand, it’s understandable that you want to be at the forefront of future research and developments.
According to Indeed, machine learning had a median salary of $153,252 in 2023 and a 53% increase in jobs since 2020. The best way to accelerate you machine learning career might just be to earn an online master’s degree.
However, getting a master’s degree can be daunting. You’re not sure how these online programs work, or if you’re getting the same quality of education. Use this guide to learn more about machine learning online master’s programs and the courses and careers that await.
A Closer Look at Three Online Machine Learning Master’s
Dive deeper into these machine learning online master’s programs with this curated spotlight. These three top-tier programs have been hand-picked for academic excellence, curriculum depth, and industry relevance.
Our spotlight features detailed profiles of each program, including highlights and what you can expect with online coursework. Each of these renowned institutions sets the standard for excellence in machine learning education.
Georgia Tech
At Georgia Tech, you’ll learn from the best of the best for a very affordable price. The online Master of Science in Computer Science program has several specializations, including machine learning.
Through this 30-credit program, you’ll explore topics like various machine learning algorithms, data science, and deep learning. You’re free to mix and match your core courses and electives to tailor your education to your future career. Certain courses are only available on-campus, so assess your options before opting for the master’s in machine learning online.
Even if your bachelor’s degree is in an unrelated field, you’re encouraged to apply. Anyone with the drive and aptitude is invited to get into computer science and machine learning.
Program Highlights
- Credits: 30
- Length: Varies
- Accreditation: SACSCOC
- Career Services: Yes
- Academic Counseling: Yes
Columbia University
Columbia University’s Fu Foundation School of Engineering and Applied Science offers a computer science master’s degree specializing in machine learning. This 30-credit fully online program equips students with the tools needed to break into various sectors.
At Columbia, breadth of knowledge is a priority. You’ll be required to take courses in systems, theory, and AI applications for a well-rounded education and a greater depth of knowledge upon graduation.
If you’ve taken enough computer science and math courses before, students with a bachelor’s degree in an unrelated field can apply for this highly technical program.
Program Highlights
- Credits: 30
- Length: Up to 5 years
- Accreditation: Middle States Commission on Higher Education (MSCHE)
- Career Services: Yes
- Academic Counseling: Yes
Drexel University
Looking for a degree specifically focused on AI and machine learning? Drexel University’s Master’s in Artificial Intelligence & Machine Learning is a perfect fit.
Three primary topics are covered: data science, theory of computation and algorithms, and application of machine learning. With these distinct focus areas, you will receive an education that combines everything from abstract theory to actual application.
Drexel University operates on a quarter-credit system, with 45 quarter-credits equal to 30 semester credits. In practice, this means that you can take more courses and complete your studies faster.
A strong background in computer science is required. To be admitted, you’ll need a bachelor’s degree in computer science or a related field, a GPA of 3.0 or higher, and work experience equivalent to Drexel’s computer science graduate degree.
Program Highlights
- Credits: 30
- Length: 2+ years
- Accreditation: MSCHE
- Career Services: Yes
- Academic Counseling: Yes
What are the Different Types of Machine Learning?
As you explore your master’s in machine learning online, you’ll encounter several different types of machine learning. From supervised learning to reinforcement learning using trial and error, each specialization offers unique applications.
Supervised Learning
Supervised learning involves training a model on labeled data or learning by example. The data set is pre-determined with input-output pairs already solved. The machine learns to identify patterns on its own, with some manual corrections.
One good example is an email spam detector. After feeding hundreds or thousands of emails manually labeled as “spam” or “legitimate” into the machine, it can then be used as a spam filter to corral “likely spam” emails into the spam folder.
Semi-supervised Learning
Somewhere between supervised and unsupervised learning, semi-supervised learning teaches machines via data that is partially labeled and partially unlabeled.
One example is image recognition. In a machine learning system, various images are added with tags describing the subject of the photos. Once the machine has a good grasp of what it’s looking at, you then add unlabeled data and allow it to make guesses at the photos’ content. Manual intervention may be needed to mark its guesses as correct or incorrect.
Unsupervised Learning
Unsupervised learning aims to find hidden patterns or structures in unlabeled data sets. With no manual instruction, it’s up to the AI to teach itself the solution, clustering and organizing the data based on patterns within.
One example is customer segmentation in marketing. By clustering similar customers based on their purchasing behavior, the algorithm uncovers distinct customer segments without any predefined labels.
Reinforcement Learning
Rewards and penalties, trial and error; is what reinforcement learning is all about. Much like how people learn themselves, the AI is left to interact with a predefined environment and receive feedback based on its choices.
An example is training a computer program to play chess. The program learns to make the most optimal moves to defeat an opponent, growing smarter with every iteration.
What Classes Will I Take in an Online ML Master’s Program?
In machine learning online master’s programs, you’ll find a variety of topics contributing to a well-rounded education and significant AI knowledge. From foundational concepts to advanced techniques, here’s a glimpse into what you can expect.
Foundations of Artificial Intelligence and Machine Learning
Before anything, you need to learn the foundations of how AI and machine learning work, and the underlying systems that power them.
This course is the bedrock of machine learning and AI, teaching fundamental concepts like the various types of machine learning outlined above and the algorithms to run these programs. You’ll gain a deeper understanding of how machines learn from data to make predictions and decisions.
Data Science and Analytics
Data science is heavily interwoven with machine learning and artificial intelligence. Many IT professionals even pursue careers strictly in data science specializations.
This course will teach you how to use AI to extract insights from complex data sets. Data wrangling, cleaning, and analysis – and how to write AI programs that can handle these tasks for you – are some of what you will learn. You’ll also learn how to use programming languages like Python and R to this effect.
Neural Networks and Deep Learning
Neural networks are an advanced, more autonomous type of machine learning algorithm. While machine learning relies heavily on labeled data sets, neural networks are advanced enough to make their own decisions.
Of course, getting to that point requires skilled programmers with mastery over machine learning. This course will give you hands-on experience in creating your own neural networks.
How Much Does a Master’s in Machine Learning Cost?
While affordable computer science programs exist, many can be a financial burden. Here’s a look at a few select machine learning programs.
| School | Program | No. of Credits | Est. Tuition |
|---|---|---|---|
| Georgia Tech | MS in Computer Science | 30 | $5,400 |
| Columbia University | Computer Science Master’s Degree | 30 | $77,915 |
| Drexel University | Master’s in Artificial Intelligence & Machine Learning | 45 | $62,820 |
Tuition and Financial Aid for Machine Learning Grad Students
If pursuing your master’s in machine learning online presents a significant financial issue, you’re not alone. Luckily, there are many resources available for computer science and data science students. If you’re looking for scholarships, fellowships, or work-study programs to fund your education, these resources can help you find them.
What Can You Do with a Master’s in Machine Learning?
After you complete your master’s in machine learning online, where do you plan to take your career? There are plenty of career options for computer science and information technology graduates. Here are a few of the most promising.
Data Science
As a data scientist, analyzing complex data sets is a major part of your daily duties. With a master’s in machine learning, building and training predictive models will become another primary task. A logical and analytical mindset will suit you well in this job, making you well-equipped to tackle challenging data problems.
Career Outlook: Data Scientist
| Median Annual Pay | |
| $108,020 | |
| 90th Percentile | |
| $184,090 | |
| Projected Growth (2022-32) | |
| 9% | |
| Top Industry | |
| Professional, Scientific, and Technical Services; Finance and Insurance |
Source: O*Net OnLine, 2023
Computer and Information Research Scientist
Driving innovation, discovering groundbreaking technologies, and pushing the boundaries of artificial intelligence – if that sounds like a fulfilling career to you, then become a machine learning research scientist.
Your work may involve designing new algorithms or optimizing existing ones, and even researching emerging technologies like quantum computing. With a master’s degree, you’ll have the expertise to make major contributions to the field of computer science.
Career Outlook: Computer and Information Research Scientist
| Median Annual Pay | |
| $145,080 | |
| 90th Percentile | |
| $233,110 | |
| Projected Growth (2022-32) | |
| 9% | |
| Top Industry | |
| Professional, Scientific, and Technical Services; Government |
Source: O*Net OnLine, 2023
Computer Systems Engineers/Architect
As a computer systems engineer or architect, you’ll design complex computing systems that integrate AI-powered solutions.
Whether you’re developing scalable cloud infrastructure, optimizing network architecture, or specializing in cybersecurity, machine learning has a place in any of these tasks. Your raw technical expertise here will be instrumental for a successful career.
Career Outlook: Computer Systems Engineer/Architect
| Median Annual Pay | |
| $104,920 | |
| 90th Percentile | |
| $174,300 | |
| Projected Growth (2022-32) | |
| 9% | |
| Top Industry | |
| Professional, Scientific, and Technical Services; Government |
Source: O*Net OnLine, 2023
Bioinformatics Technician
In the field of bioinformatics, you’ll leverage machine learning techniques to analyze biological and genetic data. From sequencing genomes to predicting protein structures, your work will contribute to advancements in healthcare, pharmaceuticals, and biotechnology.
If you’re interested in biology, data science, and artificial intelligence you are well suited for this specialized field.
Career Outlook: Bioinformatics Technician
| Median Annual Pay | |
| $70,620 | |
| 90th Percentile | |
| $155,150 | |
| Projected Growth (2022-32) | |
| 5-8% | |
| Top Industry | |
| Professional, Scientific, and Technical Services |
Source: O*Net OnLine, 2023
Information Security Engineer
AI has long played a major role in cybersecurity, and recent advances have made AI an even greater focus.
As an information security engineer, your job is to find new ways to implement machine learning in the field of cybersecurity, creating stronger security protocols and conducting vulnerability assessments.
Career Outlook: Information Security Engineer
| Median Annual Pay | |
| $104,920 | |
| 90th Percentile | |
| $174,300 | |
| Projected Growth (2022-32) | |
| 9% | |
| Top Industry | |
| Professional, Scientific, and Technical Services |
Source: O*Net OnLine, 2023
Common Questions About Machine Learning Master’s Degrees
What are the admissions requirements for an online master’s in machine learning?
If you’re planning to apply to a machine learning program, here’s what you’ll typically need.
- Relevant bachelor’s degree – Many, though not all, machine learning online master’s programs require a bachelor’s degree in a relevant field of data science or computer science.
- Minimum GPA – Depending on the institution, a GPA of 3.0 or above may be required.
- Work experience – You may need to have spent a certain number of years working in a job related to computer science or machine learning.
Will I be accepted into a machine learning master’s program without a computer science (CS) or related undergrad?
While a computer science or related bachelor’s does help, many programs listed above, like Georgia Tech and Columbia University, are willing to consider applicants without a CS undergrad. Relevant work experience and even personal passion can make a difference.
How long does it take to earn an online master’s degree in machine learning?
Generally, you can expect to complete your machine learning master’s program in 1.5 to 3 years. If you’re a part-time student, it may take up to 5 years.
Is there a benefit to earning a master’s degree in machine learning online instead of in person?
Online master’s programs offer flexibility, allowing you to study from anywhere in the world while maintaining an active computer science career. Plus, online machine learning programs offer a unique learning experience you may not get from traditional education.
Do I need to learn coding or computer programming to study machine learning?
A solid grasp of programming languages like Python, R, or Java is essential to success in machine learning fields. But if you don’t already know these coding languages, many programs will teach you the fundamentals.