Machine learning is rapidly transforming industries, driving advancements in artificial intelligence, robotics, finance, healthcare, and more. As demand for AI-powered solutions grows, companies seek highly skilled professionals who can develop, optimize, and deploy machine learning models.
Earning a master’s in machine learning can provide advanced technical skills in deep learning, neural networks, and natural language processing. These programs prepare graduates for careers in AI development, automation, and data science, offering opportunities in both research and industry applications.
This guide highlights 20 of the best master’s in machine learning programs, focusing on curriculum quality, faculty expertise, research opportunities, and industry alignment. Whether you are a student, professional, or career changer, understanding how to evaluate ML master’s programs will help you make an informed decision about your education and career path.
What to Look for in a Machine Learning Master’s Program
Choosing the right master’s in machine learning program requires careful consideration of factors such as accreditation, curriculum, faculty, and industry connections. While many programs offer coursework in artificial intelligence and machine learning, not all provide the same depth of knowledge, research opportunities, or career support.
Accreditation Considerations
- Most machine learning master’s programs are offered by regionally accredited universities, ensuring they meet recognized academic standards.
- Unlike other professional fields, there is no single programmatic accreditor for AI and ML degrees.
- Programs affiliated with strong AI research labs, National Science Foundation (NSF) grants, or IEEE partnerships often indicate academic excellence and industry relevance.
Coursework and Curriculum
A strong machine learning master’s program should include core courses covering:
- Deep learning – Understanding neural networks and training advanced AI models.
- Natural language processing (NLP) – Developing AI that can interpret and generate human language.
- AI ethics – Addressing bias, fairness, and responsible AI development.
- Big data and cloud computing – Exploring scalable solutions for machine learning applications.
Faculty and Research Opportunities
Universities with leading AI researchers and well-funded labs provide students with access to cutting-edge research and hands-on projects. Look for programs where faculty members are actively publishing in AI conferences like NeurIPS, ICML, and CVPR.
Internships and Industry Partnerships
Strong connections with major AI-driven companies can provide valuable real-world experience. Some of the best programs offer:
- Internship placements at Google, Microsoft, NVIDIA, Tesla, and other tech leaders.
- Capstone projects in collaboration with industry partners.
- AI incubators and startup accelerators for entrepreneurial students.
Online vs. On-Campus Programs
- Online programs offer flexibility for working professionals but may have fewer research opportunities.
- On-campus programs often provide hands-on learning, networking, and access to university research labs.
- Hybrid options combine online coursework with in-person research or lab work.
Understanding these factors can help students select a machine learning master’s program that aligns with their career goals, technical interests, and preferred learning format.
Top 20 Master’s in Machine Learning Degree Programs
Choosing a master’s program in machine learning is a significant step toward a career in artificial intelligence, data science, or automation. While many universities offer AI-related degrees, the programs included in this list were selected based on multiple factors, including curriculum quality, geographic diversity, graduation rates, and overall university reputation.
It is important to note that the best program for any individual depends on their specific needs, circumstances, and career goals. Some students may prioritize research opportunities, while others may focus on industry connections, flexibility, or affordability. Because of this, this list should not be viewed as a definitive ranking of the “top” programs but rather as a collection of Exceptional options for those pursuing a machine learning master’s degree.
The following programs represent a diverse range of academic institutions offering rigorous machine learning education. As you explore these options, consider visiting program pages, reviewing admission requirements, and assessing how each aligns with your personal and professional aspirations.
1. University of Pennsylvania – Philadelphia, Pennsylvania
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 97%
Machine Learning Degree Program: Robotics Master’s Specialization Artificial Intelligence and Machine Learning
Program Basics
- Degree Type: Master’s in Robotics
- Specialization Option: Artificial Intelligence and Machine Learning
- Curriculum Includes:
- Core Courses: Machine learning, vision & learning, integrated intelligence for robotics, advanced AI topics, learning in robotics, deep learning principles
- Elective Options: Machine perception, computer vision, computational learning theory, GPU programming, learning and control
- Foundational Breadth Requirement: Must complete coursework in Control or Robot Design
Program Highlights
The University of Pennsylvania’s Master’s in Robotics program, offered through the renowned GRASP Laboratory, provides students with cutting-edge training in AI, machine learning, and robotic intelligence. The program emphasizes interdisciplinary coursework that equips students to develop robots capable of making intelligent decisions based on real-world data. With a strong research component, students have opportunities to work with leading faculty and industry partners, positioning them for careers at the forefront of robotics and artificial intelligence.
About the University
The University of Pennsylvania is a prestigious Ivy League institution with a long-standing tradition of excellence in engineering and technology. Home to the GRASP Laboratory, one of the world’s leading robotics research centers, Penn Engineering offers students unparalleled resources, research opportunities, and industry connections to advance their careers in robotics and AI.
2. Duke University – Durham, North Carolina
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 96%
Machine Learning Degree Program: Artificial Intelligence – Master of Engineering
Program Basics
- Degree Type: Master of Engineering in Artificial Intelligence for Product Innovation
- Format: On-campus or online
- Duration: 12 months full-time (2-4 semesters) or 24 months part-time online
- Credit Requirements: 30 credits
- Curriculum Includes:
- 8 Technical Courses: Focused on AI, machine learning, and product innovation
- 2 Business Courses: Leadership and management training
- Capstone Project: Hands-on, real-world AI application
Program Highlights
Duke University’s Master of Engineering in Artificial Intelligence for Product Innovation is designed for students looking to build AI-powered solutions for real-world applications. The program blends strong technical coursework with leadership and business training, equipping students to drive AI advancements across industries. With a hands-on capstone project, students gain practical experience while building a portfolio that showcases their expertise in AI and machine learning.
About the University
Duke University’s Pratt School of Engineering is a leader in technological innovation and interdisciplinary research. With cutting-edge labs, world-class faculty, and strong industry connections, Duke provides an immersive educational experience that prepares students to shape the future of artificial intelligence and machine learning.
3. Northwestern University – Evanston, Illinois
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 96%
Machine Learning Degree Program: Master of Science in Machine Learning and Data Science
Program Basics
- Degree Type: Master of Science in Machine Learning and Data Science (MLDS)
- Format: Full-time, in-person
- Duration: 15 months, including a required summer internship
- Location: Northwestern University’s Evanston campus, with access to Chicago’s industries
- Class Size: Cohort of approximately 55 students
- Curriculum Includes:
- Predictive, descriptive, and prescriptive analytics
- Data engineering, AI solutions, and optimization techniques
- Practicum, capstone, and hands-on industry projects
- Case studies, guest lectures, and networking events
Program Highlights
Northwestern University’s Master of Science in Machine Learning and Data Science (MLDS) program provides a comprehensive approach to data science by integrating technical, analytical, and leadership skills. Students engage in a cohort-based learning experience, applying their knowledge to real-world business challenges through industry collaborations, hands-on projects, and a required summer internship. With a focus on ethical and responsible data science, graduates are well-equipped to lead in a rapidly evolving field.
About the University
Northwestern University’s McCormick School of Engineering is known for its interdisciplinary approach to education and research. The MLDS program leverages Northwestern’s strong industry ties and proximity to Chicago’s tech and business hubs, offering students unparalleled opportunities to work with top companies and gain practical experience in machine learning and data-driven decision-making.
4. Johns Hopkins University – Baltimore, Maryland
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 95%
Machine Learning Degree Program: Artificial Intelligence Master’s Program Online
Program Basics
- Degree Type: Master of Science in Artificial Intelligence (MSAI)
- Format: Fully online
- Curriculum Includes:
- Core topics in machine learning, natural language processing, robotics, and image processing
- Theoretical foundations balanced with real-world applications
- Elective options to tailor the degree to individual career goals
- Additional Program Options: Graduate Certificate in Artificial Intelligence
- Proficiency Exams: Available for students with industry experience to opt out of prerequisites
Program Highlights
Johns Hopkins University’s online Master’s in Artificial Intelligence is designed to equip engineers with cutting-edge AI expertise. Developed in collaboration with the Johns Hopkins Applied Physics Lab, the program offers a rigorous curriculum that blends theoretical knowledge with practical application in areas such as robotics, natural language processing, and image processing. With courses taught by leading researchers and industry experts, students gain the advanced skills needed to develop AI-driven solutions for real-world challenges.
About the University
Johns Hopkins University is a globally recognized research institution known for its leadership in science, engineering, and applied technology. The Whiting School of Engineering’s Engineering for Professionals division provides flexible, high-quality graduate education tailored for working professionals, ensuring students receive industry-relevant training that keeps pace with the rapidly evolving field of artificial intelligence.
5. Rice University – Houston, Texas
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 94%
Machine Learning Degree Program: Online Master of Data Science Specialization in Machine Learning
Program Basics
- Degree Type: Online Master of Data Science (MDS) with a Specialization in Machine Learning
- Format: Fully online
- Curriculum Includes:
- Core Machine Learning Courses:
- Statistical Machine Learning
- Natural Language Processing
- Deep Learning
- Applied Exercises: Data visualization, pattern classification, marketing optimization, financial risk management
- Core Machine Learning Courses:
- Career Outlook: High demand for machine learning experts, with projected job growth over 40% by 2024 (Forbes)
Program Highlights
Rice University’s Online Master of Data Science (MDS) with a Specialization in Machine Learning equips students with cutting-edge technical skills to leverage data science and AI in real-world applications. The program provides a flexible, rigorous education that blends statistical and computational techniques with hands-on learning. Students develop expertise in areas such as natural language processing and deep learning while applying machine learning strategies to optimize business, finance, and healthcare decision-making.
About the University
Rice University is a top-ranked research institution known for innovation and strong industry connections. Located in Houston, a hub for healthcare, energy, and technology, Rice provides students with valuable exposure to real-world business applications. The university’s commitment to data science and AI education ensures graduates are prepared to lead in the rapidly evolving field of machine learning.
6. Carnegie Mellon University – Pittsburgh, Pennsylvania
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 93%
Machine Learning Degree Program: Master’s in Machine Learning
Program Basics
- Degree Type: Master’s in Machine Learning (MSML)
- Format: In-person, full-time
- Duration: Typically 16 months (three to four semesters)
- Curriculum Includes:
- Core Courses: Six required courses covering foundational ML and statistics
- Electives: Three courses allowing specialization in various ML areas
- Practicum: Internship or research project in machine learning (summer semester)
- Financial Support: No departmental funding; students must cover tuition and expenses
Program Highlights
Carnegie Mellon University’s Master’s in Machine Learning is a rigorous, research-driven program designed to equip students with advanced skills in ML theory and applications. The program blends a strong theoretical foundation with practical experience, including a required practicum where students apply their knowledge in industry or research settings. With access to world-renowned faculty and CMU’s cutting-edge ML research, graduates are well-prepared for careers in academia, tech, and AI-driven industries.
About the University
Carnegie Mellon University is a global leader in computer science and artificial intelligence, consistently ranked among the top institutions for machine learning research. Housed within the School of Computer Science, the Machine Learning Department provides students with unparalleled access to pioneering faculty, state-of-the-art facilities, and strong industry partnerships, positioning graduates at the forefront of AI innovation.
7. Georgia Tech – Atlanta, Georgia
Institution Type: 4-year, Public
Graduation Rate: 93%
Machine Learning Degree Program: Online Master of Science in Computer Science (OMSCS)
Program Basics
- Degree Type: Online Master of Science in Computer Science (OMSCS) with a Specialization in Machine Learning
- Format: Fully online
- Credit Requirements: 15 hours in Machine Learning specialization, plus 15 hours of free electives
- Curriculum Includes:
- Core Courses (6 hours): Algorithms and foundational machine learning coursework
- Electives (9 hours): Options include deep learning, reinforcement learning, natural language processing, machine learning for robotics, big data analysis, and more
- Free Electives (15 hours): Any approved OMSCS courses
Program Highlights
Georgia Tech’s Online Master of Science in Computer Science (OMSCS) with a Specialization in Machine Learning provides students with advanced knowledge in AI-driven technologies while maintaining the flexibility of an entirely online format. The program offers rigorous coursework in algorithms, machine learning, and data analysis, allowing students to tailor their education through specialized electives. With access to Georgia Tech’s renowned faculty and research resources, graduates gain the expertise needed to excel in AI, data science, and related fields.
About the University
Georgia Institute of Technology is a top-ranked research university recognized for its excellence in engineering, computer science, and artificial intelligence. The OMSCS program is a pioneer in high-quality, affordable online education, making advanced computing degrees accessible to students worldwide. Through its cutting-edge curriculum and industry connections, Georgia Tech prepares graduates to lead in the evolving world of AI and machine learning.
8. University of Southern California – Los Angeles, California
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 92%
Machine Learning Degree Program: Master of Science in Applied Data Science
Program Basics
- Degree Type: Master of Science in Applied Data Science
- Format: Available online via DEN@Viterbi and on-campus
- Duration:
- Full-time: 1.5 – 2 years
- Part-time: 2.5 – 3 years
- Curriculum Includes:
- Python programming for data science
- Data management and big data infrastructure
- Machine learning and data mining techniques
- Artificial intelligence applications
- Data visualization and analytics for real-world problem-solving
Program Highlights
The University of Southern California’s Master of Science in Applied Data Science program prepares students from diverse academic backgrounds to become skilled data scientists. The curriculum covers the full data science pipeline, from programming and database management to machine learning and artificial intelligence. Through USC’s DEN@Viterbi online platform, students receive the same high-quality education as on-campus students, with access to expert faculty and industry-relevant coursework. Graduates are well-positioned for careers in fields such as aerospace, software design, cybersecurity, bioinformatics, and consulting.
About the University
The University of Southern California is a world-renowned research institution known for its excellence in engineering and technology. The Viterbi School of Engineering, home to the Applied Data Science program, is a leader in innovative education and industry partnerships. Through cutting-edge research and a strong professional network, USC provides students with the tools and connections needed to excel in the rapidly growing field of data science.
9. Northeastern University – Boston, Massachusetts
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 90%
Machine Learning Degree Program: Master of Professional Studies in Applied Machine Intelligence
Program Basics
- Degree Type: Master of Professional Studies (MPS) in Applied Machine Intelligence
- Format: On-campus in Boston, MA
- Duration: 12 – 18 months
- Enrollment Options: Full-time or part-time
- Curriculum Includes:
- Applied machine learning and AI
- Data management, analysis, and visualization
- Ethical AI and human-centered design
- Experiential projects with industry partners (e.g., Google, Deloitte, Uber)
Program Highlights
Northeastern University’s Master of Professional Studies in Applied Machine Intelligence is a multidisciplinary, experience-based program designed to prepare students for AI-driven careers in industries such as healthcare, finance, and human resources. The curriculum emphasizes technological, data, and human literacy, equipping students with the skills to integrate AI into business strategies. Through real-world projects with industry leaders, students gain hands-on experience applying machine learning to solve complex problems.
About the University
Northeastern University is a global leader in experiential learning and research, with a strong emphasis on industry partnerships and innovation. Located in Boston, a major hub for technology and business, Northeastern provides students with access to cutting-edge research facilities, networking opportunities, and career connections across diverse sectors.
10. Stevens Institute of Technology – Hoboken, New Jersey
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 90%
Machine Learning Degree Program: Machine Learning Master’s Program
Program Basics
- Degree Type: Master of Science in Machine Learning
- Format: Available both on-campus and online
- Curriculum Includes:
- Deep learning theory and practical applications
- Computer vision, speech recognition, and natural language processing
- Robotics, finance, bioinformatics, and healthcare applications
- Hands-on research and lab experiences
- Career Outcomes: Graduates work as machine learning engineers, data scientists, research scientists, and R&D engineers at companies like Amazon, Bloomberg, Facebook, Google, IBM, and Intel
Program Highlights
The Master of Science in Machine Learning at Stevens Institute of Technology provides students with a rigorous foundation in both theoretical and applied machine learning. The curriculum equips students with the skills necessary to work in high-demand industries, from AI-powered finance to robotics and bioinformatics. Located in the heart of the New York City metro area, Stevens offers access to top tech firms and a strong professional network. Students can also benefit from cooperative education programs, research opportunities, and state-of-the-art labs.
About the University
Stevens Institute of Technology is a leading research university known for its innovation in AI, engineering, and data science. With its prime location near NYC’s thriving tech industry, Stevens provides unparalleled career placement opportunities, cutting-edge research facilities, and industry partnerships that prepare graduates for success in the evolving field of machine learning.
11. University of Maryland – College Park, Maryland
Institution Type: 4-year, Public
Graduation Rate: 89%
Machine Learning Degree Program: Applied Machine Learning – Master of Science
Program Basics
- Degree Type: Master of Science in Applied Machine Learning
- Format: In-person (College Park campus, evening classes)
- Duration: Less than two years
- Credit Requirements: 30 credits (10 courses)
- Curriculum Includes:
- Foundations in mathematics, statistics, and programming
- Advanced topics such as deep learning, optimization, big data analysis, and signal/image understanding
- Applications in computer vision, natural language processing, robotics, and data science
- Program Focus: Practical knowledge, no research opportunities
Program Highlights
The University of Maryland’s Master of Science in Applied Machine Learning is designed for working professionals seeking hands-on expertise in machine learning. The program provides a rigorous technical curriculum, covering fundamental and advanced machine learning concepts with applications across industries such as healthcare, finance, telecommunications, and security. With courses taught by UMD faculty and industry experts, students gain the skills necessary for careers as data scientists, machine learning engineers, and information engineers.
About the University
The University of Maryland, College Park, is a top-tier research institution known for its excellence in computer science, engineering, and applied mathematics. The Science Academy, housed within the College of Computer, Mathematical, and Natural Sciences, offers innovative graduate programs that prepare students for high-demand careers in emerging fields such as data science and artificial intelligence.
12. University of Wisconsin-Madison – Madison, Wisconsin
Institution Type: 4-year, Public
Graduation Rate: 89%
Machine Learning Degree Program: Electrical & Computer Engineering: Machine Learning & Signal Processing MS
Program Basics
- Degree Type: Master of Science in Electrical and Computer Engineering – Machine Learning & Signal Processing
- Format: In-person, accelerated program
- Duration: 12–16 months (course-based, no thesis required)
- Credit Requirements: 30 graduate credits
- Curriculum Includes:
- Machine learning and artificial neural networks
- Signal processing and information processing
- Hands-on professional development project (internship, co-op, or independent project)
- Tuition: $1,200 per credit
- STEM OPT Eligible: Yes
- F-1 Visa Support: Yes
Program Highlights
The University of Wisconsin–Madison’s Master of Science in Electrical and Computer Engineering – Machine Learning & Signal Processing is designed to fast-track students into data science and AI-focused careers. With a rigorous, hands-on curriculum, the program emphasizes practical problem-solving, coding, and quantitative thinking. Students gain industry-relevant experience through professional development projects and courses taught by faculty conducting pioneering research in AI and signal processing.
About the University
The University of Wisconsin–Madison is a top-ranked public research institution known for excellence in engineering and technology. With strong industry ties and a prime location in a growing tech hub, UW–Madison provides students with exceptional career opportunities at leading companies such as Boeing, Apple, Lockheed Martin, and Northrop Grumman.
13. George Washington University – Washington, District of Columbia
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 85%
Machine Learning Degree Program: Online Master of Engineering in Artificial Intelligence and Machine Learning
Program Basics
- Degree Type: Master of Engineering in Artificial Intelligence & Machine Learning
- Format: 100% online
- Credit Requirements: 30 credits (10 courses, 3 credits each)
- Curriculum Includes:
- Neural networks, deep reinforcement learning, and adversarial networks
- Natural language processing, computer vision, and robotics
- Pattern recognition, autonomous systems, and data visualization
- Ethical considerations and societal impacts of AI technologies
- Learning Options: Synchronous and asynchronous coursework for flexibility
Program Highlights
The George Washington University’s online Master of Engineering in Artificial Intelligence & Machine Learning provides students with a strong foundation in advanced AI algorithms, computational methods, and real-world applications. The curriculum covers essential topics such as deep learning, pattern recognition, and robotics while also emphasizing ethical considerations in AI. Designed for working professionals, the program offers flexible learning options and prepares graduates for careers in technology, healthcare, finance, and transportation or for further research in AI-driven fields.
About the University
The George Washington University, located in Washington, D.C., is a prestigious research institution known for its innovation and interdisciplinary approach to engineering and technology. The School of Engineering & Applied Science provides students with industry-relevant expertise and access to cutting-edge advancements in AI, fostering career growth in a rapidly evolving technological landscape.
14. University of Washington – Seattle, Washington
Institution Type: 4-year, Public
Graduation Rate: 84%
Machine Learning Degree Program: Master of Science in Artificial Intelligence and Machine Learning for Engineering
Program Basics
- Degree Type: Master of Science in Artificial Intelligence and Machine Learning for Engineering
- Format: Fully online (part-time) or in-person (full-time option available)
- Curriculum Includes:
- AI & ML applications in engineering fields such as robotics, manufacturing, and chemical processes
- Foundational skills in mathematics, coding, and AI ethics
- Hands-on applied capstone project
- Stackable certificate structure, allowing flexible learning pathways
- Eligibility: Not eligible for F1/J1 visa holders; available to non-U.S. citizens studying online from outside the U.S.
Program Highlights
The University of Washington’s Master of Science in Artificial Intelligence and Machine Learning for Engineering is designed for working engineers seeking to integrate AI and ML tools into their field. The flexible curriculum allows students to build a strong foundation in AI/ML while tailoring their studies to specific engineering applications. With stackable graduate certificates and an applied capstone project, students gain hands-on experience solving real-world engineering challenges using AI.
About the University
The University of Washington is a globally recognized research institution known for its excellence in engineering and technology. The College of Engineering fosters interdisciplinary collaboration, offering students access to cutting-edge research and industry connections in AI, robotics, and advanced manufacturing.
15. Syracuse University – Syracuse, New York
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 81%
Machine Learning Degree Program: Applied Human-Centered Artificial Intelligence Master’s Degree
Program Basics
- Degree Type: Master’s in Applied Human-Centered Artificial Intelligence
- Format: On-campus
- Credit Requirements: 31 credits (11 courses)
- Duration: Can be completed in as little as one year
- Curriculum Includes:
- Primary Core (21 credits): Courses in deep learning, natural language processing, human-AI interaction, responsible AI, and text mining
- Secondary Core (9 credits): Choose from tracks in Data Science, Cloud Computing, or a self-guided selection of IST graduate courses
- Exit Requirement: Applied Human-Centered AI Portfolio
Program Highlights
Syracuse University’s Master’s in Applied Human-Centered Artificial Intelligence (HCAI) prepares students to build AI applications with a strong emphasis on ethics and human collaboration. The curriculum balances technical expertise with applied learning, allowing students to gain hands-on experience in AI development, large language models (LLMs), and machine learning. Through experiential learning, internships, and research opportunities, students graduate with a robust portfolio and in-demand skills applicable across industries such as healthcare, business intelligence, and software development.
About the University
Syracuse University’s School of Information Studies (iSchool) is a leader in data science and AI education, offering a unique interdisciplinary approach that integrates technology with human-centered design. With strong industry partnerships, cutting-edge research, and dedicated career services, Syracuse provides students with the resources and connections needed to thrive in the fast-growing AI field.
16. Drexel University – Philadelphia, Pennsylvania
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 78%
Machine Learning Degree Program: Master’s (MS) in Machine Learning and Artificial Intelligence
Program Basics
- Degree Type: Master of Science in Artificial Intelligence and Machine Learning (MSAIML)
- Format: Available online and on-campus
- Duration: Full-time (2-3 years), Part-time (2-4 years)
- Credit Requirements: 45 quarter credits (equivalent to 30-33 semester credits)
- Concentrations:
- Computational Track: Focuses on deep AI/ML theory, algorithms, and computation
- Applied Track: Emphasizes AI/ML applications in data science, HCI, and business intelligence
- Capstone: Two-term hands-on collaborative project
- Graduate Co-op Option: Available for full-time, on-campus students
Program Highlights
Drexel University’s MS in Artificial Intelligence and Machine Learning provides a strong foundation in AI/ML with an interdisciplinary curriculum covering algorithms, data science, cognitive computing, and domain-specific applications. Students can customize their learning through elective courses or a modular certificate pathway. The program culminates in a hands-on capstone project, ensuring practical industry experience. The quarter system structure enables students to explore more topics and specialize in areas aligned with their career goals.
About the University
Drexel University is a leader in experiential education, offering one of the nation’s top co-op programs that connects students with industry-leading companies. The College of Computing & Informatics is known for its research in AI, data science, and cybersecurity, providing students with access to world-class faculty and cutting-edge technology.
17. Milwaukee School of Engineering – Milwaukee, Wisconsin
Institution Type: 4-year, Private not-for-profit
Graduation Rate: 72%
Machine Learning Degree Program: M.S. in Machine Learning
Program Basics
- Degree Type: Master of Science in Machine Learning (MSML)
- Format: Fully online with synchronous live lectures (two nights per week)
- Duration: Flexible, depending on course load
- Credit Requirements: 32 credits (eight 4-credit courses)
- Stackable Certificates:
- Applied Machine Learning Graduate Certificate
- Machine Learning Engineering Graduate Certificate
- Prerequisites:
- Bachelor’s degree in a technical field
- Object-oriented programming experience (C++, Python, etc.)
- Undergraduate coursework in probability, statistics, and integral calculus
Program Highlights
The MS in Machine Learning at Milwaukee School of Engineering is tailored for working professionals looking to apply advanced machine learning concepts immediately in their fields. The program features hands-on learning, small class sizes, industry applications in every course, and access to Rosie the supercomputer. Students gain expertise in deploying production-quality machine learning solutions using state-of-the-art software and parallel computing hardware.
About the University
Milwaukee School of Engineering (MSOE) is a highly regarded institution known for its strong industry partnerships and hands-on approach to engineering education. The university provides a collaborative learning environment with cutting-edge technology and research opportunities, ensuring graduates are well-prepared for the rapidly evolving field of machine learning.
18. University of Arizona – Tucson, Arizona
Institution Type: 4-year, Public
Graduation Rate: 66%
Machine Learning Degree Program: MS in Information Science: Machine Learning
Program Basics
- Degree Type: Master of Science in Information Science (Machine Learning)
- Format: Fully online or hybrid (first year online, second year on-campus)
- Duration: 2 years
- Credit Requirements: Not specified
- STEM-OPT Eligibility: Up to 3 years (for hybrid students)
- Tuition:
- Online: $11,000
- Hybrid: $34,176 (saves $67,750 compared to full-time US master’s programs)
- Application Deadline: March 4, 2025
- GRE/TOEFL Requirement: Not required for selected countries
Program Highlights
The MS in Information Science with a specialization in Machine Learning, offered in collaboration with the University of Arizona and Great Learning, equips students with essential skills in data science, artificial intelligence, and machine learning. The program balances theoretical foundations with practical applications, allowing students to engage in hands-on projects and case studies. The hybrid option provides cost savings and the opportunity for on-campus learning in the U.S., making students eligible for extended STEM-OPT work opportunities.
About the University
The University of Arizona is a top-ranked public university recognized globally for its research and innovation. With strong industry connections and a commitment to interdisciplinary learning, Arizona provides an ideal environment for students seeking a career in AI and machine learning. The university’s partnerships with leading tech companies such as Google, Microsoft, Tesla, and NASA offer students valuable networking and career opportunities.
19. San Jose State University – San Jose, California
Institution Type: 4-year, Public
Graduation Rate: 65%
Machine Learning Degree Program: MS in Statistics with a Specialization in Machine Learning
Program Basics
- Degree Type: Master of Science in Statistics with a Specialization in Machine Learning
- Department: Mathematics and Statistics
- Format: On-campus
- Duration: Not specified
- Credit Requirements: Not specified
- Application Deadline: Varies (refer to SJSU Graduate Admissions)
Program Highlights
The MS in Statistics with a Specialization in Machine Learning at San José State University (SJSU) equips students with advanced statistical and programming skills necessary for developing machine-learning algorithms. This specialization is designed for those seeking careers as statisticians in business, government, or industry, focusing on AI-driven data analysis and automation. The curriculum provides hands-on experience in probability, statistics, and algorithm development to enable students to create models that learn and improve autonomously.
About the University
San José State University, located in the heart of Silicon Valley, offers students unparalleled access to top technology firms and data-driven industries. With strong industry connections and a commitment to applied learning, SJSU provides an ideal environment for students seeking careers in machine learning and artificial intelligence.
20. Colorado State University Global – Aurora, Colorado
Institution Type: 4-year, Public
Graduation Rate: 47%
Machine Learning Degree Program: Online Master’s in Artificial Intelligence (AI) & Machine Learning
Program Basics
- Degree Type: Master of Science in Artificial Intelligence and Machine Learning
- Institution: Colorado State University Global
- Format: 100% Online
- Total Credits: 30
- Cost per Credit: $675
- Start Dates: Monthly (12 starts per year)
- Accreditation: Higher Learning Commission (HLC)
Program Highlights
The MS in Artificial Intelligence and Machine Learning at CSU Global provides students with advanced knowledge in AI, machine learning, computer vision, and programming. The curriculum focuses on real-world applications, teaching students how to apply mathematical, statistical, and programming techniques to develop AI-driven solutions that model human behavior. Graduates will be prepared for high-paying careers in AI and machine learning, with demand for AI professionals projected to grow rapidly.
About the University
Colorado State University Global is a leader in online education, offering flexible, career-focused degree programs. As part of the CSU system, it is recognized for academic excellence, affordability, and innovation, making it an excellent choice for working professionals looking to advance in AI and machine
With so many excellent machine learning master’s programs available, the best choice depends on individual career goals, learning preferences, and program features. Some students may prioritize research opportunities at top AI labs, while others may seek industry partnerships for hands-on experience. Additionally, factors such as program flexibility, online options, and faculty expertise can all play a role in the decision-making process.
Prospective students should review program details, visit official university pages, and reach out to admissions offices to gather more information. Using this list as a starting point, students can compare programs and identify the machine learning master’s degree that best aligns with their personal and professional aspirations.
Online vs. On-Campus Master’s in Machine Learning
When choosing a master’s in machine learning program, students must decide whether an online or on-campus format best fits their learning style, career goals, and personal circumstances. Both options offer unique advantages and trade-offs, and some universities even provide hybrid models that combine online coursework with in-person research opportunities.
Best Online ML Master’s Programs for Flexibility
Online machine learning programs provide flexibility for working professionals and career changers who need to balance education with other commitments. Some of the best online ML programs offer:
- Self-paced or asynchronous coursework, allowing students to learn on their own schedules.
- Live virtual lectures and interactive sessions, providing engagement with faculty and peers.
- Access to online research labs and collaborative projects, ensuring hands-on experience even in a remote setting.
Hybrid Options: Combining Online Learning with Hands-On Research
Some universities offer hybrid master’s programs that blend online coursework with in-person research components. These programs allow students to complete lectures remotely while participating in lab work, internships, or intensive on-campus residencies. Hybrid formats are particularly useful for students who want the flexibility of online learning without sacrificing hands-on experience.
Which Format Is Right for You?
- Choose an online program if you need flexibility, prefer self-paced learning, or want to continue working while earning your degree.
- Choose an on-campus program if you value face-to-face networking, access to AI research labs, and direct mentorship from faculty.
- Consider a hybrid program if you want a balance between remote coursework and in-person engagement.
By carefully evaluating these factors, students can select the program format that best supports their educational and career goals in the field of machine learning.
FAQ: Choosing the Best Master’s in Machine Learning
What’s the difference between a Master’s in Machine Learning and a Master’s in Artificial Intelligence?
A Master’s in Machine Learning focuses on statistical modeling, deep learning, and algorithms that enable computers to learn from data. In contrast, a Master’s in Artificial Intelligence covers a broader range of AI topics, including robotics, computer vision, and natural language processing. Machine learning is a subset of AI, meaning there is significant overlap between the two degrees, but AI programs often have a wider scope.
How long does a Master’s in Machine Learning take to complete?
Most master’s programs in machine learning take 1.5 to 2 years to complete for full-time students. Some programs offer accelerated options that can be completed in as little as one year, while part-time students may take up to three or more years to finish their degree.
Do I need programming experience before enrolling in an ML master’s program?
Yes, most programs expect applicants to have prior programming experience, particularly in languages like Python, R, C++, or Java. Some programs may require proficiency in data structures, algorithms, and linear algebra before admission. However, certain universities offer preparatory coursework or bridge programs for students transitioning from non-technical backgrounds.
Are online ML master’s degrees respected by employers?
Yes, as long as the degree comes from an accredited and reputable university. Many top institutions offer online machine learning programs that maintain the same academic standards as their on-campus counterparts. Employers often focus more on skills, experience, and portfolio projects rather than whether a degree was earned online or in person.
Next Steps: Applying to a Machine Learning Master’s Program
Once you’ve identified the machine learning master’s programs that align with your career goals, the next step is preparing a strong application. Admissions requirements vary by university, but most programs evaluate applicants based on academic background, technical skills, and relevant experience.
Researching Application Requirements
- Academic prerequisites – Many programs require a bachelor’s degree in computer science, engineering, mathematics, or a related field. Some may accept applicants from other backgrounds if they have strong programming and analytical skills.
- GPA requirements – Competitive programs often look for a minimum GPA of 3.0 or higher, though top-tier universities may have higher expectations.
- GRE scores – Some programs require the GRE, while others have made it optional or removed it entirely. Check individual program requirements before applying.
Preparing for Technical Admissions Tests (If Required)
Some machine learning programs require applicants to complete a technical assessment or demonstrate proficiency in programming, data structures, or mathematical concepts. Reviewing topics such as linear algebra, probability, Python, and machine learning algorithms can improve readiness for these assessments.
Building a Strong ML Portfolio
A well-developed portfolio can significantly strengthen an application, particularly for candidates without extensive academic or professional experience in AI. Consider including:
- Kaggle competition projects showcasing applied machine learning skills.
- GitHub repositories featuring personal AI models, deep learning applications, or predictive analytics projects.
- Internship or research experience related to AI, data science, or automation.
Finalizing Your Application
- Prepare a compelling statement of purpose outlining your motivation, career goals, and why you’re interested in the program.
- Secure strong letters of recommendation from professors, employers, or mentors in the AI and tech field.
- Ensure all application materials, including transcripts and test scores, are submitted before the deadline.
A well-prepared application increases the chances of admission into a competitive machine learning program. By focusing on academic readiness, technical skills, and hands-on projects, applicants can position themselves for success in this fast-growing field.
