BestOnlineCollege.org is an advertising-supported website. Many of the school and program listings that appear on this site are from partners who compensate us, and this compensation may affect how, where, and in what order listings appear (such as featured placements). This compensation does not influence our editorial content, evaluations, or rankings, which are determined independently using publicly available data. We do not review or feature every school or program available in the marketplace. Our goal is to provide accurate, unbiased information so you can make informed decisions. Read our full Advertiser Disclosure.
A standalone bachelor’s degree titled “machine learning” is uncommon. At the undergraduate level, machine learning is usually taught as a concentration, minor, or elective sequence inside a broader major – most often computer science, data science, or artificial intelligence. That is not a downgrade. It reflects how the field works: machine learning depends on programming, mathematics, and statistics foundations that a four-year major is built to deliver, and the specialization typically lands in the junior and senior years once those foundations are in place.
This page explains which undergraduate pathways lead to machine learning work, what coursework to look for in each, and how to compare them honestly rather than searching for a degree title that few schools award.
Rarely as a standalone title. Most undergraduates reach machine learning through a computer science, data science, or artificial intelligence bachelor’s degree that includes machine learning coursework, a concentration, or an elective sequence. Standalone machine learning degrees are far more common at the master’s level.
Computer science is the most common and most flexible pathway, because it delivers the programming and systems foundation that machine learning work assumes. Data science is a strong alternative if you want more statistics earlier. An artificial intelligence major covers machine learning alongside the wider field.
Expect calculus, linear algebra, and probability and statistics. Linear algebra matters most, because it is the language models are written in. Programs differ in whether they require these before machine learning coursework or teach them in sequence, so check the prerequisite chain.
Approximately 120 semester hours, typically four years full-time. The machine learning content is generally a subset of that – a concentration or elective block rather than the whole major.
Generally not. Employers hiring for machine learning work look at coursework, projects, and demonstrated ability more than the degree title. A computer science degree with machine learning electives and a strong project portfolio is a well-recognized profile.
It depends on the role. Engineering-side positions that build and serve models are frequently entered with a bachelor’s. Research and applied-modeling positions more often expect graduate training. See Online Master’s in Machine Learning for that comparison.
For a full map of this program area, start here: Machine Learning Program Guide
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:
Schools without enough outcome data appear after ranked schools, without a score. Advertising never affects these rankings. Read the full methodology.
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University CommissionIPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:Accreditor: New England Commission on Higher EducationIPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
This is the most common route. A computer science major delivers programming, data structures and algorithms, systems, and databases, then offers machine learning as upper-division electives or a formal concentration. The advantage is breadth: if you decide machine learning is not what you want, a computer science degree still supports a wide range of software work. The thing to verify is that the department actually offers enough machine learning electives to matter, and that they are reliably taught online rather than listed in the catalog but only offered on campus.
See the computer science program guide for the broader major, and the data science concentration within computer science for the computing-heavy data specialization.
A data science major front-loads more statistics and data handling than computer science does, and usually includes machine learning as required coursework rather than as an elective. This suits students who are more interested in modeling and inference than in building software systems. The tradeoff is typically less depth in algorithms, systems, and software engineering, which matters if you are aiming at machine learning engineering roles.
An artificial intelligence major covers machine learning alongside search, knowledge representation, planning, natural language processing, robotics, and AI ethics. It gives you the widest view of the field and is the better choice if you are not yet sure which part of AI you want to work in. If you already know you want the modeling and statistics core, a narrower pathway gets you deeper faster. See the artificial intelligence program guide for details, and AI degree vs data science degree for a direct comparison.
| Course Topic | What You Learn |
|---|---|
| Linear Algebra | Vectors, matrices, and decompositions – the representation models are built on |
| Probability & Statistics | Distributions, estimation, hypothesis testing, and reasoning under uncertainty |
| Programming & Data Structures | Python or a comparable language, plus the data structures algorithms depend on |
| Introduction to Machine Learning | Supervised and unsupervised methods, model evaluation, overfitting and regularization |
| Neural Networks & Deep Learning | Network architectures, training procedures, and applications to images and text |
| Data Engineering Fundamentals | Collecting, cleaning, and structuring data for model training |
| Capstone or Applied Project | An end-to-end project from problem framing through a validated model |
The math sequence is what determines how quickly you reach machine learning coursework, and it is the most common place plans slip. Before you enroll, confirm:
If you are moving faster than a standard four-year plan, compare accelerated machine learning programs.
Transfer credit can substantially shorten a bachelor’s, particularly for general education and the introductory math sequence. Rules vary by institution, so confirm:
See Affordable Online Machine Learning Degrees for more on managing total program cost.
There is no programmatic accreditor for machine learning degrees or concentrations. Verify that the institution holds accreditation from a recognized institutional accreditor – such as HLC or SACSCOC – through the U.S. Department of Education database.
A bachelor’s in a related major with machine learning coursework is a workable entry point for engineering-side roles that build and serve models. A master’s in machine learning is the more common route into applied modeling and research roles, and it is also the level at which the standalone degree title actually exists. Whether the extra time and cost make sense depends on which of those role types you are aiming at.
Compare degree options:
Data verified: August 10, 2026. Salary, employment, and tuition figures on this page are sourced from the U.S. Bureau of Labor Statistics (OEWS May 2025; Employment Projections 2024–2034) and the U.S. Department of Education College Scorecard (2023 cohort). The source agency and data year are cited inline with every statistic.
