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Key takeaway: Machine learning is the engineering and statistical core of artificial intelligence – the part concerned with building models that learn patterns from data. As a standalone degree title, “machine learning” is mostly a master’s-level offering (typically an MS in Machine Learning). At the bachelor’s level, most students get their machine learning training inside a computer science, data science, or artificial intelligence degree that includes machine learning coursework. There is no programmatic accreditor for machine learning degrees, so institutional accreditation is the check that matters. Compare accredited programs below.
Machine learning sits where statistics, programming, and linear algebra meet. Coursework generally builds the mathematical foundation first – calculus, linear algebra, and probability – then layers on supervised and unsupervised learning methods, neural networks, and the engineering practices needed to get a model into production. Accredited online programs generally deliver the same curriculum and degree titles as campus programs; browse the best accredited online colleges to compare schools that offer them.
These accredited schools offer online programs. Request information to compare programs, costs, and formats.
Every school list on this site is ordered by the BOC Score, computed from the most recent school-level data published by the U.S. Department of Education (College Scorecard and IPEDS). To qualify, a school must be currently operating and accredited by an agency recognized by the U.S. Department of Education. Each eligible school is then scored on five measures, percentile-ranked against schools at the same credential level:
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Source:IPEDSCollege Scorecard
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University CommissionIPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Machine learning graduates work in software companies, healthcare systems, financial institutions, government research labs, manufacturing, and consulting. The occupations below are commonly associated with machine learning coursework. Median annual wages come from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program.
Entry requirements differ sharply across these occupations. Computer and information research scientist roles, which had a median annual wage of $140,300 (BLS OEWS, May 2025), commonly expect a graduate degree, and research-heavy positions often expect a doctorate. Data scientist roles, at a median annual wage of $120,230 (BLS OEWS, May 2025), are frequently entered with a master’s degree or with a bachelor’s plus substantial applied experience. Software developer roles, at a median annual wage of $135,980 (BLS OEWS, May 2025), are the most common bachelor’s-level entry point for people who want to build machine learning systems rather than research new methods. Statistician roles, at a median annual wage of $105,650 (BLS OEWS, May 2025), typically expect graduate coursework in statistics. Individual outcomes vary by employer, geography, and experience.
A machine learning degree trains you to build and evaluate models that learn from data. As a standalone title it is mostly a master’s-level program, usually awarded as an MS in Machine Learning. At the bachelor’s level, most students reach the same skills through a computer science, data science, or artificial intelligence degree with machine learning coursework and electives.
Machine learning programs cover linear algebra, multivariable calculus, and probability and statistics as a foundation, then supervised and unsupervised learning, model evaluation, neural networks and deep learning, and the engineering practices used to deploy and monitor models. Most programs finish with a capstone or applied project.
Artificial intelligence is the broader field and the broader degree – it covers machine learning alongside search, knowledge representation, planning, robotics, natural language processing, and AI ethics. Machine learning is the modeling and algorithms core inside that field, and it is more often offered as a master’s-level specialization than as a broad undergraduate major. If you want the wider survey, see the artificial intelligence program guide; if you want depth in the statistical and engineering core, machine learning is the narrower path. For a direct comparison of related credentials, see AI degree vs data science degree.
No. Many people working in machine learning hold a computer science, statistics, mathematics, physics, or engineering degree and picked up machine learning through electives, graduate coursework, or on-the-job project work. A machine learning degree can shorten that path and signal the specialization clearly, but employers generally screen on demonstrated ability to build and evaluate models, which is why portfolio work matters as much as the degree title.
No. There is no recognized programmatic accreditor specific to machine learning degrees. Verify that the institution holds accreditation from a recognized institutional accreditor such as HLC or SACSCOC through the U.S. Department of Education database. Some computer science departments hold ABET computing accreditation for their CS degrees, which is a separate check and does not extend to a machine learning specialization.
Machine learning curricula generally move through three layers, and the first one surprises people who come in expecting to start with models.
The first layer is mathematics. Linear algebra is not optional here – matrix operations are how models are represented and trained. Multivariable calculus supplies the gradients that training algorithms follow. Probability and statistics supply the reasoning about uncertainty that separates a model you can trust from one you cannot. A program that lets you skip all three is teaching you to call libraries, not to do machine learning.
The second layer is methods. Supervised learning covers regression and classification, including linear models, tree ensembles, and support vector machines. Unsupervised learning covers clustering and dimensionality reduction. Model evaluation covers cross-validation, bias and variance, overfitting, and the metrics appropriate to different problem types. Neural networks and deep learning then extend the same ideas to architectures used for images, sequences, and language.
The third layer is engineering and judgment. This is where curricula differ most. Stronger programs include data pipelines, feature engineering, experiment tracking, model deployment, and monitoring for drift after a model is in production – often labeled MLOps. They also cover fairness, interpretability, and the failure modes that come from training on data that does not represent the population a model will be applied to.
At the master’s level, expect roughly 30 to 36 credits, a mix of required core and electives, and either a thesis track or an applied capstone track. At the bachelor’s level, expect roughly 120 credits, of which the machine learning content is a concentration or elective sequence within a broader major.
The work splits roughly into three shapes. Research positions develop new methods and usually sit in industrial research labs or academia; these are the roles where a doctorate is common. Applied modeling positions – often titled data scientist or machine learning scientist – take a business or clinical problem, decide whether machine learning is the right tool, build and validate a model, and defend the result. Engineering positions – often titled machine learning engineer, which in practice is usually a software developer role with a modeling emphasis – build the systems that serve models reliably at scale.
Titles are inconsistent across employers, and the same title can mean very different jobs at two companies. Read the responsibilities rather than the title. For general labor-market context on the occupations machine learning graduates commonly enter, see the Bureau of Labor Statistics Occupational Outlook Handbook.
Machine learning or a related field? Choose machine learning for depth in the statistical and algorithmic core. Consider artificial intelligence for a broader survey of the field, computer science if you want a general software and systems foundation with machine learning as an elective path, the data science concentration within computer science if you want computing-heavy data work, or data analytics if your interest is drawing and communicating conclusions from data rather than building models.
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