Quick Highlights:
- Our #1 ranking college for a master’s in machine learning is University of Arizona, followed by Georgia Institute of Technology.
- ML programs teach computers to learn from data, while AI covers broader topics like language processing and robotics.
- Top ML programs focus on data analysis, predictive modeling, and pattern recognition.
- Programs can be completed in 15-24 months, often requiring a thesis or capstone project.
- Graduates have strong job prospects in tech, earning higher salaries and working for top companies.
Enrolling in a Master’s program in Machine Learning is a forward-thinking decision for those eager to be at the forefront of technology and innovation. As industries across the world increasingly rely on artificial intelligence (AI) and machine learning (ML) to drive decision-making, automate processes, and analyze big data, the demand for skilled professionals in these fields is skyrocketing.
According to the U.S. Bureau of Labor Statistics, employment for computer and information research scientists, which includes machine learning engineers, is projected to grow by 26% from 2023 to 2033—much faster than the average for all occupations. This growth is fueled by the expanding need for AI and machine learning solutions in sectors such as healthcare, finance, automotive, and retail, where companies are utilizing ML to streamline operations, personalize services, and enhance user experiences.
Moreover, the global machine learning market size is expected to reach $225.91 billion by 2030, growing at a compound annual growth rate (CAGR) of 36.2%, as reported by Fortune Business Insights. This rapid expansion underscores the enormous potential for ML professionals, with roles such as machine learning engineers, data scientists, and AI specialists increasingly in high demand.
For students considering the field, pursuing a Master’s in Machine Learning is not only an investment in specialized, high-demand skills but also a gateway to cutting-edge technologies that are shaping the future of business and society.
This ranking was created in February 2024 and updated in 2025. The data was accurate at time of publication.
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Last updated: April 4, 2025
What are the Best Machine Learning Degree Programs?
At CollegeRank, we start with data and evaluation informed by experience. Our trusted team of professional researchers, writers, and editors develop each ranking and resource with future learners in mind. We are invested in highlighting degree programs and options that are known for quality, affordability, accessibility, and return on investment.
Our rankings and resources are continually updated with the most recent available data and information on trends in higher education. Our unique and proven ranking methodology sets us apart because our ranking system is based on the following three aspects:
At CollegeRank, we strive to do our best to guide you and your family toward a fruitful academic career. The pursuit of knowledge is a noble one, and we want to help you reach your goals.
To supply you with the best of the best in Master’s in Machine Learning degree programs, we considered the following points when compiling this list, such as:
- The school’s ability to provide a quality Master’s degree program in the field of Machine Learning,
- Offering various learning degree formats, such as online, on-campus, or hybrid coursework,
- Taught by professionals in the field of Machine Learning,
- Offers financial aid opportunities, such as federal loans, scholarships, and grants,
- Displays proper accreditation, pursuant to the field of Machine Learning,
- Prepares graduates for career placement in Machine Learning.
Please feel free to visit our dedicated methodology page for a step-by-step breakdown. For questions, comments, badge downloads, or data corrections, please feel free to reach out to us at editor@www.collegerank.net.
Related (Campus):
- Is a CS Master’s Worth It?
- Best Graduate Programs for Computer Engineering
- Best Online Master’s in Information Assurance and Security
Related (Online):
Average Net Tuition Disclaimer: The tuition amounts given are estimates and subject to change.
University of Arizona
The University of Arizona offers a STEM designed master’s in information technology with a focus on machine learning. The program is offered on-campus and students attend courses face to face. Students can complete their degree in just 18 months.
The machine learning subplan if focused on teaching students how to interpret and management large amounts of data by automating processes. Students will take core courses in machine learning and choose from a variety of focused electives. Courses include:
- Data Analysis and Visualization
- Introduction to Machine Learning
- Artificial Intelligence
All students complete either an internship or a capstone project. Students may have the opportunity to intern at top organizations like Amazon or Tesla. The capstone project allows students to showcase the skills they have mastered in the program.
New students are accepted for the fall and spring semesters each year. Potential students need to have a bachelor’s degree from an accredited college or university. Applicants should have completed undergraduate level mathematics coursework. They should also have computer programming experience.
Georgia Institute of Technology
The College of Computing at Georgia Tech features an online Master of Science in Computer Science with a Machine Learning specialization. The OMS CS program was created in collaboration with Udacity and AT&T to be an affordable degree students could earn exclusively through a massive online format.
The OMS CS program is a flexible program. Students can adjust their course schedule to have time for full-time employment. Most students can complete their degree in three years, but there is flexibility if students need some extra time. The Machine Learning concentration area requires 15 credit hours of specialized course content. Students can choose from a variety of options including:
- Machine Learning for Robotics
- Data and Visual Analytics
- Deep Learning
While we calculated the ROI based on the average graduate student tuition rate for the school, the specific tuition rate for the OMS CS program may be less. Students need 30 credit hours to graduate. Students are also eligible for financial aid.
University of Maryland
The College of Computer, Mathematical, and Natural Sciences at the University of Maryland features an innovative Master of Professional Studies in Machine Learning. This ML degree program is a 30-credit hour non-thesis program. Students will gain the skills they need to be successful in areas like information engineering and data science.
Throughout the program, students engage in cutting-edge technical coursework. They develop a strong foundation in:
- mathematics
- statistics
- computer programming
Students will apply the applications of machine learning to areas like natural language processing and robotics.
The machine learning graduate program is designed for working professionals. Both full and part-time program options are available. Courses take place on-campus with face-to-face instruction. Course offerings include:
- Computer Vision
- Probability and Statistics
- Advanced Machine Learning
To be considered for the program, applicants must have a bachelor’s degree from an accredited university with at least a 3.0 GPA. Applicants should also submit a resume and a description of research or work experience. They should also be proficient in programming languages and have taken previous coursework in quantitative studies. The GRE is optional applicants of the machine learning program.
Milwaukee School of Engineering
If you are looking for an online masters degree in machine learning from one of the best tech schools in the country, here it is! The MS in Machine Learning from Milwaukee School of Engineering was designed for working professionals who want to gain advanced skills in ML. MSOE is well respected in the field of ML. They offered the first undergraduate computer science program focused on AI. Their ML graduate degree program is one of the few programs in the country focused on the application of ML to industrial problems. Students benefit from technical content and industry applications in every course. They also get to apply their learning in a hands-on environment using Rosie the supercomputer!
Courses are delivered in a convenient synchronous format. Students can meet with faculty online or in-person. In addition to earning a master’s degree, students can also earn two “stackable” certificates. The courses are built into the program so there are no additional requirements.
The Applied Machine Learning Graduate Certificate and the Machine Learning Engineering Graduate Certificates add value to the degree and can help boost a resume. Students can also choose elective courses that align with their professional goals. All students complete a machine learning capstone course that includes a final report and presentation to faculty and industry representatives.
Admission into the machine learning program is competitive. Applicants should have a bachelor’s degree in a technical field and computer programming experience. Both full and part-time students are encouraged to apply. Full-time students can complete their degree in two years.
Drexel University
Drexel University’s College of Engineering has a long history of producing machine learning experts. Graduates of their Master of Science in Machine Learning Engineering have gone on to work for top companies like Google and Microsoft.
Drexel’s ML program provides students with knowledge in three pillars. These include:
- Fundamentals
- Implementation
- Applications
Students will gain an understanding of modern machine learning to develop solutions to problems. They will also learn how to use industry-leading software like TensorFlow and scikit-learn to create machine learning systems. Finally, the program will show students how ML can be applied to a variety of industries including bioengineering and cybersecurity.
Drexel’s program has many unique opportunities for students. All students in the ML program can participate in research. Full-time students or those planning to pursue a PhD are encouraged to base their master’s thesis on an area of faculty research. There are a variety of ML research labs including:
- Adaptive Signal Processing and Information Theory Research Gruop
- Computational Image Sequence Analysis Laboratory
- Center for Electric Power Engineering
Students can also take advantage of the graduate co-op. Through this optional experience, students can apply theories learning in class to a work experience. These three or six-month work experiences can help provide context and build professional skills. Students can also use resources at the Steinbright Career Development Center to help with a job search or resume writing.
Drexel’s ML program takes place on-campus and is open to both full and part-time students. Full-time students can complete their degree in just 18 months. Part-time students can finish in three to four years.
Northwestern University
The McCormick School of Engineering at Northwestern University features a Master of Science in Machine Learning and Data Science. The ML degree is an accelerated program that students can complete in just 15 months. This full-time program is offered on-campus and students will complete courses during the day. Students move through the curriculum as a cohort, allowing them to form a tight-knit community of like-minded professionals.
The program is limited to just 55 students. The small cohort size supports individualized instruction and attention. The curriculum includes a required summer internship and two industry-supplied projects. Coursework covers areas like:
- maching learning
- artificial intelligence
- data engineering
Students benefit from industry collaboration while they complete their projects, working under the guidance of business and technical advisors. They complete their first project during the first three quarters of the program and their final capstone project during the final quarter. These hands-on learning opportunities prepare students for success after graduation.
Due to the small cohort size, admission to the ML program is competitive. Potential students can submit their applications September 1 through January 15th. Students interested in receiving a scholarship should have their application submitted by December 1st. The program is open to students from a variety of backgrounds. Northwestern holds a two-week bootcamp/orientation at the start of the program to make sure everyone has a solid foundation of key concepts before starting the program.
Carnegie Mellon University
The Machine Learning Department at Carnegie Mellon University is the world’s first academic department of machine learning. The department was created with the goal of bringing together an interdisciplinary group of researchers with a passion for statistics and machine learning.
The Master of Science in Machine Learning is a program primality focused on coursework although research opportunities are available. Machine learning courses include:
- Probabilistic Graphical Models
- Machine Learning in Practice
- Advanced Deep Learning
All students complete a one-semester full-time practicum experience. This could be an internship or research related to machine learning. Most students complete their practicum over the summer.
Motivated students can complete their degree in three semesters. Applications are accepted in December and new students begin the program in the fall. Applicants should have a strong background in computer science and some experience with probability and statistics. Courses are offered in-person so students must be able to come to campus to complete their degree.
Stevens Institute of Technology
Stevens Institute of Technology offers a masters degree in machine learning focused on theoretical foundations and the practical aspects of ML. The program is a great choice for students with an undergraduate degree in an area like computer science or electrical engineering (or a closely related field.)
Stevens’ offers one of the most flexible machine learning programs in our ranking. While students can choose to take courses online or on-campus, they can also choose to take a full or part-time courseload. Interested in a career in research? Students can also choose to complete a thesis as part of the program. Internships are also available for students who want to get professional hands-on experience.
Students can expect to work alongside recognized experts in the field. They’ll take core courses like:
- Statistical Machine Learning
- Applied Modeling and Optimization
- Text Mining and Information Retrieval
Stevens’ graduates are some of the most prepared in the field. Their location and proven history of career placement success gives students valuable networking opportunities. Graduates have what it takes to be successful in industry, academia, or research.
Columbia University
Columbia University in the City of New York offers a fully online master’s in machine learning. The program is designed for students who want to build up on their knowledge and skills in machine learning and its applications.
The ML curriculum is comprised of 30 credit hours. Required courses include:
- Machine Learning Theory
- Neural Networks Deep Learning
- Computational Aspects of Robotics
Students choose two courses from a wide variety of elective offerings.
Admission to the program is competitive. Most applicants have an undergraduate degree in computer science. Applicants with a degree in another area should have completed at least six prerequisite courses. Most students admitted to the program have an undergraduate GPA of at least a 3.5. The GRE is not required for admission. Applications are accepted on a rolling basis and new students are accepted each semester.
Duke University
The Pratt School of Engineering at Duke University is a world class research center offering a Master’s degree in Machine Learning/Big Data. Students can choose from either a research-oriented Master of Science (MS) or an industry focused Master of Engineering (MEng). Key courses include:
- Probabilistic Machine Learning
- Programming, Data Structures, and Algorithms in C++
- Natural Language Processing
The MS program allows students to choose from a coursework only option or complete a project or thesis. The MEng program includes a built-in internship and business courses.
One of the unique features about the machine learning program at Duke is that students can participate in cross-campus initiatives. There are several initiatives focused on data science and machine learning. One of the most popular is the Rhodes Information Initiative at Duke or iiD. iiD offers innovative programs for students including Data+. Data+ is an intensive 10-week summer research experience for students who want to learn about new data-driven approaches.
New machine learning students are admitted for the fall semester. Admission is competitive. The GRE is optional for admission, but students should have a minimum undergraduate GPA of a 3.2 for the MS program and a 3.4 for the MEng. Most students will finish their degree in four semesters of study.
How to Choose a Graduate Machine Learning Program
A Master’s in Machine Learning offers a powerful opportunity for students to dive deep into one of the most transformative fields in modern technology. As industries across the globe increasingly adopt artificial intelligence (AI) and machine learning to streamline operations, solve complex problems, and drive innovation, the demand for skilled professionals is higher than ever. From healthcare to finance to autonomous vehicles, machine learning is powering advancements in virtually every industry.
Choosing the right program can be a pivotal decision in setting up a successful career in this rapidly evolving field. Here are five critical considerations to keep in mind when choosing a Master’s in Machine Learning degree program.
- Curriculum Emphasis on Algorithms, Data Structures, and Statistical Methods: A top-tier machine learning program should have a rigorous curriculum that covers core concepts like algorithms, data structures, and statistical methods. These are the building blocks of machine learning, enabling students to understand how models work, how data is processed, and how to fine-tune algorithms to solve real-world problems. A program that emphasizes a strong foundation in these areas will better prepare you for practical applications in the field.
- Hands-On Experience with Machine Learning Frameworks and Tools: Look for programs that provide hands-on experience with popular machine learning frameworks and tools like TensorFlow, PyTorch, and Scikit-learn. Practical skills in implementing machine learning models are essential, and a program that includes coding assignments, project-based learning, or access to cloud computing resources (e.g., AWS, Google Cloud) will help you gain experience with the tools that are commonly used in the industry.
- Specialization Options in Areas like Deep Learning, Natural Language Processing, or Reinforcement Learning: Machine learning is a broad field, and many programs offer specializations in specific areas like deep learning, natural language processing (NLP), or reinforcement learning. If you have a particular area of interest, choose a program that offers advanced coursework and research opportunities in those fields, as specialization can give you an edge in niche industries such as AI research, autonomous systems, or AI-driven healthcare.
- Opportunities for Research and Industry Collaboration: Given the rapid advancements in machine learning, programs that offer strong research opportunities or collaborations with industry are crucial. Look for programs that facilitate access to faculty-led research projects, internships with tech companies, or partnerships with research institutions. These opportunities will allow you to work on cutting-edge machine learning applications and build professional connections in the field.
- Strong Career Support and Industry Connections: The right program should offer strong career support services, including networking opportunities with leading tech companies, recruitment fairs, and internship placement programs. An established alumni network can also be beneficial, as connections within the industry can lead to mentorship opportunities, job placements, and collaborations on projects.
Frequently Asked Questions
Yes, a masters degree in AI and machine learning is worth it for many IT professionals. You’ll develop advanced skills that top companies are looking for. The knowledge you gain will help you tackle real-world problems and contribute to cutting-edge research. If you are passionate about machine learning and career advancement in tech, a master’s in AI and Machine Learning can help you reach your goals.
A degree in machine learning can be challenging. It involves understanding complex algorithms, statistics, and programming languages like Python. Success in a machine learning degree program depends on your willingness to learn new concepts. You’ll need to practice coding and work on problem-solving skills. While it may be challenging at times, the rewards can be significant!
While the Bureau of Labor Statistics doesn’t specifically report earnings for a Machine Learning Specialist, they do provide an average salary for the closely related role of data scientist. The average wage for a data scientist according to the BLS is $108,020/year. Payscale reports the average base salary for a Machine Learning Engineer is $120,147/year. While these figures may vary depending on education and skillset, they give you a guide for what to expect with a master’s in machine learning.
Machine learning is definitely in high demand! Many industries, including healthcare, finance, e-commerce, and more, rely on machine learning algorithms to analyze data, make predictions, and automate tasks. As technology advances, the need for professionals with machine learning skills continues to grow. Earning a master’s in machine learning can lead to promising career opportunities and job security.