Best Online Bachelor’s in Machine Learning: 2025 Rankings and ML Degree Directory

Machine learning (ML) is one of the hottest jobs in tech. According to Statista, the ML market is projected to see an annual growth rate of 32.41% from 2025-2031, leading to a market volume of $568.32 billion USD by 2031. Unsurprisingly, job postings for AI and ML experts have also seen explosive growth—in 2024, postings for roles requiring AI skills grew 61% year-on-year. Demand for machine learning engineers is particularly strong. At LinkedIn, staff software engineers in machine learning were making up to $336,000 in 2025.

If you’re excited about the possibilities of a machine learning career, this guide is for you. Choosing the right bachelor’s program in ML from a well-respected university is a critical step in securing your first job. That’s why we’ve profiled the 5 Best Online Bachelor’s in Machine Learning Degrees and the 10 Best Campus-Based Bachelor’s in Machine Learning Degrees in the USA. Explore our summaries to learn more about the unique strengths of each program. Or jump ahead to our directory to find an ML undergraduate degree in your home state. Since machine learning bachelor’s programs are still rare, our directory includes both online and on-campus options.

What is a Bachelor’s in Machine Learning?

A bachelor’s degree in machine learning focuses on the theory, design, and application of algorithms that allow computers to learn from data and make decisions or predictions. Machine learning students learn how to develop and optimize ML models and deploy ML solutions across a wide range of real-world scenarios. Think of search engines & content recommendations, ChatGPT, image & speech recognition, self-driving vehicles, fraud detection, supply chain optimization, IBM Watson Health, and more. All of these applications are built on a foundation of machine learning.

To that end, bachelor’s programs in machine learning often concentrate on developing skills in:

• Probability, statistics, and linear algebra—the underpinnings of machine learning
• Programming (e.g. Python)
• Data preprocessing (e.g. cleaning, transformation, reduction, etc.) and feature engineering—transforming raw data into usable formats
• Supervised learning (with labeled data), unsupervised learning (with unlabeled data), and reinforcement learning
• Deep learning & neural networks
• Model tuning & evaluation
• Deployment techniques using cloud platforms (e.g. AWS, Azure, Google Cloud, etc.) and ML frameworks (e.g. TensorFlow, PyTorch, Keras, etc.)
• Applications in robotics, computer vision, Natural Language Processing (NLP), and more

Rankings Methodology

The team at Degree Prospects is committed to discovering undergraduate machine learning programs that deliver a significant Return on Investment (ROI). That’s why our rankings of the 5 Best Online Bachelor’s in Machine Learning Degrees and the 10 Best Campus-Based Bachelor’s in Machine Learning Degrees are based on a weighted algorithm that considers the following factors:

• Machine Learning Focus: We only considered bachelor’s degrees with a dedicated major or concentration in machine learning (ML). These will contain advanced coursework in ML-specific areas like natural language processing (NLP), computer vision, robotics, and the like.
• Academic Reputation & Awards: All of the schools in our rankings are non-profit, regionally accredited institutions. Our algorithm gives an additional boost to universities with strong U.S. News & World Report rankings in computer science. It also factors in the university’s machine learning research output.
• Real-World Outcomes: Our statisticians drew on data sources such as IPEDS and College Scorecard to analyze program completion rates and earnings potential 1-5 years after graduation. This gave us a sense of whether the bachelor’s degree was worth the price tag.
• Overall Performance: The final step was to rank the schools that demonstrated strong performance across multiple categories. That’s why Carnegie Mellon University is #1 in our list of the best campus-based bachelor’s programs in ML. It’s a top performer in post-graduation earnings, ML research, and academic reputation.

Use these rankings to narrow your search and create a shortlist of potential ML programs! Unlike other generic AI degree websites, our detailed profiles are designed to give you the best shot at impressing admissions committees. We’ve highlighted machine learning research opportunities for undergraduates, capstone project examples, combo BS/MS pathways, honors programs, and a whole lot more.

Note: Honorable mention goes to universities that offer a dedicated minor in machine learning. For instance, Georgia Tech’s interdisciplinary Minor in Applications of Artificial Intelligence and Machine Learning can be added to a large number of bachelor’s programs.

Which Undergraduate Major is Best for Machine Learning?

Standard BS Majors

Machine Learning (ML) is typically offered as a concentration or specialization within a more traditional major such as computer science, electrical & computer engineering, data science, or statistics. However, you will also see it combined with artificial intelligence coursework in newer AI/ML undergraduate programs.

All of these majors will include training in ML topics & skills, but it pays to compare the coursework & degree title against job descriptions. For example, a computer science degree may be more useful for software engineering roles than a generic AI/ML major. In contrast, a computer engineering degree may be ideal for machine learning work in the automotive sector. Use the profiles in our online and on-campus rankings and the curriculum links in our directory to get a sense of the program’s focus.

BS Major	Sample Courses in the Major	Sample Job Titles
Computer Science	Software engineering, algorithms, operating systems, databases, data structures, programming, data manipulation, linear algebra, probability, statistics, data mining + ML courses.	ML, NLP, and Computer Vision Engineers; Software Engineers; AI/ML Research Assistants
Computer Engineering	Hardware-focused coursework (e.g. circuit design) + courses in computer architecture, embedded systems, engineering applications, signal processing + ML courses.	ML, Computer Vision, Autonomous Systems, and Robotics Engineers
AI/ML	Computer science foundations, programming, data structures, algorithms & optimization, linear algebra, probability, statistics, AI foundations; AI tools & frameworks + ML courses	ML, NLP, and Computer Vision Engineers; Applied AI Developers; AI Application Developers
Data Science	Statistical analysis, computational thinking, data mining, data modeling, probability theory, statistical inference, linear algebra, programming + ML courses	Data Scientists; Business Intelligence (BI) Developers
Statistics	Linear & logistic regression, multivariate analysis, Bayesian methods, probability theory, statistical inference, applied linear algebra, statistical data science, programming + ML courses.	Data Scientists; Statistical Programmers; Biostatisticians

Related Majors

Machine learning has applications that extend far beyond the realm of computer science. In response to demand, a number of universities now allow students to add an ML concentration to an impressive number of engineering and tech-related undergraduate degrees. For example, Carnegie Mellon University’s concentration in Machine Learning (ML) can be tacked on to any major in its School of Computer Science (SCS). Here is a list of related majors that may benefit from training in machine learning techniques:

• Human-Computer Interaction
• Robotics
• Information Technology
• Cognitive Science
• Computational Biology
• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• Mechanical Engineering
• Industrial Engineering
• Applied Math
• Business Analytics
• GIScience
• Environmental Data Science

Remember, too, that you can customize a bachelor’s degree with minors. For instance, if you are enrolled in a computer science program with a concentration in ML, you might wish to add a minor in statistics for more rigorous statistical work or data science for in-depth training in data modeling. You could even consider a double major. Make a list of your top 5 career goals and then find the program that most closely matches the job requirements.

Undergraduate Machine Learning Curriculum and Coursework

Common ML Courses in a BS Degree

There is no standardized template for a bachelor’s degree in machine learning. Some programs will allow you to dive deep into ML fundamentals and advanced electives. Other degrees will limit their ML concentration to 2-3 courses. Duke’s Bachelor of Science in Computer Science – AI and ML is a good example of a typical comp sci degree. It features two core courses in AI & machine learning fundamentals (e.g. Applied ML), two electives (e.g. Computer Vision, NLP, Robot Learning, etc.), and one additional course in computer science or a related field. We recommend you look for undergraduate programs that cover the following:

Machine Learning Core

• Introduction to Machine Learning (e.g. supervised & unsupervised learning)
• Applied Machine Learning
• Deep Learning & Neural Networks

Mathematical Foundations

• Linear Algebra
• Probability & Statistics
• Optimization

Programming & Tools

• Data Structures & Algorithms
• Programming for Data Science & ML (e.g. Python)
• Scalable Machine Learning (e.g. cloud computing, ML model deployment, etc.)

Machine Learning Electives

• Natural Language Processing (NLP)
• Computer Vision
• Robotics
• Reinforcement Learning
• Human-Computer Interaction (HCI)
• Advanced Data Science

Applied Learning

• Undergraduate Research Opportunities
• Senior Capstone

Machine Learning Skills to Master

Jobs that require a bachelor’s degree in machine learning often want to see candidates who have a solid foundation in statistics, data science, and ML algorithms; strong programming skills in Python; experience with deep learning frameworks & libraries; knowledge of cloud-based deployment; and an understanding of SQL and big data tools. Prioritize ML undergraduate programs that provide training in the following skills:

• Python: The dominant language for ML model development and scripting
• Statistics & Probability: e.g. evaluation, inference, and model validation
• Data Preprocessing & Feature Engineering: e.g. cleaning, transforming, and preparing data
• SQL & Database Skills: e.g. accessing and manipulating structured data
• Machine Learning Algorithms: e.g. regression, classification, clustering & neural nets
• Deep Learning, LLMs & ML Frameworks: e.g. TensorFlow, PyTorch, Keras
• Cloud Platforms & Scalable ML: e.g. AWS, GCP, Azure, SageMaker
• Model Evaluation & Performance Metrics: e.g. cross-validation, ROC AUC & overfitting checks
• Software Engineering Fundamentals: e.g. version control and modular coding & debugging
• Data Science/Big Data Frameworks: e.g. Spark & Hadoop for scalable data processing

How to Gain Real-World Experience in a Machine Learning Degree

The best bachelor’s programs in machine learning will provide students with plenty of opportunities to work on real-world projects. Roughly 70-90% of job postings for entry-level or early career ML roles will ask for some prior experience. During your undergraduate studies, these can take the form of:

• Internships & Co-Op Experiences: Programs like Virginia Tech’s BS in Computer Engineering – Machine Learning include a mandatory internship/co-op experience in the curriculum. But you don’t have to be limited by the coursework. Most reputable Departments of Computer Science or Electrical & Computer Engineering can connect you to industry partners. In our rankings, we’ve also highlighted unique opportunities like Duke’s DTech program.
• Capstone Projects: The best BS programs in ML will feature a final capstone that will allow you to tackle a real-world problem or comprehensive research project. For instance, UW-Madison’s BS in Computer Engineering – ML & Data Science and CU Boulder’s BSCS – AI & ML offer some impressive and unusual capstone experiences. Before applying for a BS, ask the program coordinator for a list of recent BS capstone projects (cf. BU’s ECE Senior Design Capstone examples). It’s a quick way of determining how technical the program is going to be!
• Self-Directed Learning: Whether you are an on-campus or online student, you will have plenty of opportunities to impress employers. Join the student-run ML or AI club and start building projects. Get involved in programming, coding & algorithmic thinking competitions (some Departments will sponsor student travel to these contests). Build your own GitHub portfolio showing ML systems work. Contribute to open-source ML projects & libraries (e.g. TensorFlow).
• Undergraduate Research: Even if you are not considering graduate study, we still suggest pursuing undergraduate research opportunities. ML is a research-heavy field and a number of ML jobs want to see evidence of theoretical work. Prestigious schools like Carnegie Mellon University offer programs such as the Summer Undergraduate Research Fellowship (SURF). But you can create your own opportunities by getting in touch with faculty in ML, AI, and data science research institutions. For instance, labs within the UC Davis Machine Learning and AI Group are open to collaborating with BS students.
• Research & Teaching Assistantships: Although Research Assistant (RA) and Teaching Assistant (TA) roles are usually reserved for graduate students in machine learning, there are some universities that will consider undergraduates. For example, Duke’s Department of Computer Science runs an Undergraduate Teaching Assistant (UTA) program for motivated CS students.
• Honors Programs: Schools like UC San Diego, UT Austin, the University of Washington, and UC Davis have developed honors programs for high-achieving undergraduates in machine learning. Honors programs could provide you with the chance to work on advanced ML research projects, a senior thesis, experiential learning opportunities, portfolio-building, and more. If you have a high GPA, talk to the BS program coordinator about the nuts & bolts of applying.

Online vs. On-Campus Bachelor’s Degrees in Machine Learning

In our directory, you’ll find a complete list of on-campus, hybrid, and online bachelor’s programs with a focus on machine learning. To give you an even deeper understanding of these options, we’ve divided our rankings into the 5 Best Online Bachelor’s in Machine Learning Degrees and the 10 Best Campus-Based Bachelor’s in Machine Learning Degrees. So the question becomes: which format is the right choice for your needs?

• On-Campus: Campus-based programs are going to be the best pick for a highly technical field like machine learning. On-campus students have access to prestigious ML research labs & institutions, technical resources, honors programs, TA & RA opportunities, internships, networking events, industry visits, and a lot more. In our rankings, you’ll notice that a number of on-campus ML degrees also have one-of-a-kind capstone experiences. Anyone considering graduate school, an ML job with a high-flying tech company, or research-heavy roles should prioritize on-campus bachelor’s programs in ML.
• Online: Virtual undergraduate programs in machine learning have the advantage of being convenient, cost-effective, and flexible. They may offer part-time plans of study. They usually have generous credit transfer policies. And they may be ideally suited to working professionals, community college graduates, and those with previous experience (e.g. military servicemembers & veterans). If you are contemplating an online program, prioritize ones that include capstone projects, virtual internships, and experiential learning opportunities. And ask if the online professors are also teaching the on-campus courses. Any adjunct faculty members should be seasoned pros who are leaders in their field.

What Comes Next? Jobs & Career Paths for Machine Learning Graduates

Common Career Paths for ML Graduates

The most common career paths for undergraduates who have specialized in machine learning include software & ML engineering roles, domain-specific roles (e.g. NLP engineers), ML-focused research positions, and graduate school (e.g. MS or PhD). We’ve listed 10 popular job titles for ML graduates below, but it’s important to check individual job listings. Machine learning is a rapidly evolving field, so companies are still playing around with job descriptions & responsibilities. For example, an ML engineer at a MAANG company may be expected to focus on deep & segmented areas of machine learning. An ML engineer at a startup company may have to wear multiple hats and work on a wide variety of software development, DevOps, and MLOps tasks.

Popular Job Titles for ML Graduates

Machine Learning Engineer

ML engineers design, build, and maintain ML systems, developing ML algorithms that can learn & make predictions and deploying these models into production. It’s a hybrid role that involves software engineering, data science, and machine learning expertise. It may be challenging to secure this role straight out of university, so talk to industry mentors about their journey to ML engineering.

• Entry-Level Salary Range: $115k-$180k
• Experience Level: 1-3 Years; 3-5 Years for Specialized Roles or Larger Companies

Software Engineer w/ ML Experience

Software engineers (SWEs) with machine learning experience may be responsible for designing & building scalable software systems that incorporate ML models. They could be collaborating with data scientists and ML engineers to develop data pipelines, optimize performance, and maintain ML-enabled features across applications. Software engineering is a good stepping stone to an ML engineering role!

• Entry-Level Salary Range: $110k-$150k
• Experience Level: 0-3 Years

Data Engineer w/ ML Experience

Data engineers with machine learning experience help design, build & maintain scalable data pipelines and infrastructures that enable ML models to function effectively. They could be collaborating with data scientists and ML engineers in areas such as data ingestion, transformation, and feature engineering. Data engineering is a good stepping stone to an ML engineering role!

• Entry-Level Salary Range: $80k-$120k
• Experience Level: 0-3 Years

Data Scientist

Data scientists are trained to analyze complex datasets, build predictive models, and produce data-driven insights using machine learning & statistical methods to support business decisions. Some data science positions may require a master’s degree or PhD. Other jobs will be open to applicants with a relevant bachelor’s degree.

• Entry-Level Salary Range: $110k-$119k
• Experience Level: 0-3 Years; 3-5 Years for More Senior Positions

Computer Vision Engineer

Computer vision engineers know how to develop, optimize, and deploy deep learning models for image and video analysis. They have specialist ML skills in areas like object detection, segmentation, and real-time inference. Look for BS programs that offer computer vision electives and opportunities to work on industry projects.

• Entry-Level Salary Range: $115k-$140k
• Experience Level: 0-3 Years (Focus on Computer Vision Projects & Internships)

Natural Language Processing (NLP) Engineer

NLP engineers create algorithms and models that enable computers to process and understand human language. They develop, build, and fine-tune these language models for tasks & applications like text classification, chatbots, machine translators, and sentiment analysis. Look for BS programs that include some training in linguistic principles.

• Entry-Level Salary Range: $117k-$124k
• Experience Level: 0-3 Years (Focus on NLP Projects & Internships)

Robotics Engineer

Robotics engineers know how to design, build, test, and maintain robots and intelligent robotic systems, integrating ML algorithms, control systems, and perception modules. Look for a BS in Computer Engineering or Robotics with a relevant ML concentration and robotics-focused research labs and institutions.

• Entry-Level Salary Range: $106k-$122k
• Experience Level: 0-3 Years (Focus on Robotics Projects & Internships)

ML Solutions Architect

ML solutions architects are responsible for creating end-to-end ML solutions, guiding the design, infrastructure, and deployment of these solutions and considering all components like data engineering, MLOps, and the user interface. They’re able to keep an eye on both business objectives and technical execution.

• Entry-Level Salary Range: $115k–$125k; $147k-$280k at Prestigious Tech Companies
• Experience Level: 2-5 Years

AI Application Developer

AI application developers are software professionals who build & implement AI systems into software applications. Unlike ML engineers, who focus on creating & optimizing machine learning models, AI application developers are much more focused on making AI accessible and useful for the end user.

• Entry-Level Salary Range: $85k-$120k; $120-$160k for Mid-Level
• Experience Level: 0-2 Years (Focus on Hands-On AI/ML Projects & Internships)

ML Research Assistant

ML research assistants support academic & industry research through experimentation, publishing papers, and developing prototype ML algorithms. Employers will usually be looking for MS or PhD graduates, but BS graduates with strong research credentials could be considered for certain openings.

• Entry-Level Salary Range: $99k-$155k+
• Experience Level: 0-2 Years (Master’s or PhD Strongly Preferred)

Industries Hiring ML Graduates

Machine learning has applications across a wide array of industries. Technology & software companies may have the most pressing needs, but you’ll find that ML skills are in high demand almost everywhere. Here are the top 10 industries hiring applicants with real-world experience and a reputable degree in machine learning (BS, MS, or PhD).

Technology & Software

Tech giants and software vendors need ML experts to develop intelligent products, personalize user experiences, automate decision-making, and optimize large-scale systems across search, recommendations, advertising, and infrastructure.

Finance & Banking

ML skills are in high demand for fintech roles in quant trading, fraud detection, risk modeling, and algorithmic trading. Hedge funds and banks tend to offer top-tier compensation packages.

Healthcare & Life Sciences

Across the healthcare sector, ML is now being used for medical imaging, diagnostics, patient monitoring, drug discovery, and administrative automation. ML is also seeing rapid growth in biosciences.

Retail & E-commerce

The need for ML in retail & E-commerce is being driven by recommendation engines, demand forecasting, customer segmentation, and supply-chain optimization.

Automotive & Transportation

ML-related roles in automotive & transportation companies include work in computer vision, autonomous vehicle development, and vehicle optimization systems.

Manufacturing & Industrial Automation

In manufacturing realms, ML is being used for tasks like predictive maintenance, quality control, robotics, and process optimization.

Telecommunications

Telecommunication companies rely on ML for network optimization, predictive maintenance, and customer service automation.

Energy & Utilities

Energy & utility companies use ML in the service of smart grids, energy optimization, resource exploration, and supporting renewable systems.

Consulting & Professional Services

Big-name consulting firms like Deloitte, Accenture, IBM, PwC, and others often hire ML professionals for enterprise-level solutions.

Government & Defense / Aerospace

Government and defense sectors deploy applied ML in areas like cybersecurity, defense systems, threat detection, and aerospace analytics.

FAQ

All Bachelor’s in Machine Learning Degree Programs