Master’s in Machine
Learning Online

<a href="https://www.onlinemasterscolleges.com/masters-in-machine-learning-online/">Master's in Machine Learning Online</a>

College Name & Address	Tuition	Graduation Rate
#1 University of North Carolina, Chapel Hill 103 South Bldg Cb 9100, Chapel Hill, North Carolina 27599	$10,552	91%
#2 Georgia Tech 225 North Ave, Atlanta, Georgia 30332	$14,064	90%
#3 CSU Global 585 Salida Way, Aurora, Colorado 80011	$9,000	NA
#4 Penns State University 201 Old Main, University Park, Pennsylvania 16802	$21,960	71%
#5 Columbia University West 116 St and Broadway, New York, New York 10027	$47,600	95%
#6 Duke University 103 Allen Bldg, Durham, North Carolina 27708	$55,680	95%
#7 Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213	$45,037	90%
#8 Stevens Institute of Technology Castle Point On Hudson, Hoboken, New Jersey 07030	$36,680	85%
#9 Drexel University 3141 Chestnut St, Philadelphia, Pennsylvania 19104	$35,181	69%
#10 University of Rochester Wilson Blvd. – Wallis Hall, Rochester, New York 14627	$51,705	87%

Source – Integrated Post Secondary Education Data System & University Data
*Tuition rates are for in-state and per year. Program specific rates may apply.
**NA – data not available

Curriculum and Skills Gained in Machine Learning Programs

Many companies are ramping up their workforce to take advantage of concepts of Machine Learning, Artificial Intelligence, Big Data, and other similar topics to leverage their businesses. Enrolling in a machine learning online course is essential to be in tandem with the job market requirements and steer a career to a better place. A Masters in Machine Learning online program can help understand the theory and practical applications of all these techniques in detail. During the degree, understand and implement techniques like regression, supervised clustering, naïve Bayes, etc. While the curriculum from program to program can be different, some courses within the degree program are common. Here are some courses one can expect to see as part of a machine learning master’s online:

Introduction to Machine Learning

This course provides a foundational understanding of key concepts and algorithms in machine learning. Students explore supervised and unsupervised learning, model evaluation, and feature engineering. The course sets the stage for advanced topics, equipping learners with essential skills for subsequent coursework and real-world applications.

Foundational and in-depth concepts of Machine Learning

This course explores core principles and advanced techniques. Covering topics such as algorithms, model optimization, and ethical considerations, it equips students with a comprehensive understanding of machine learning, preparing them for sophisticated applications and research in the field.

Predictive Analytics 

This course includes various statistical techniques, including multiple regression, logistic regression, discriminant analysis, generalized linear models, and Cox proportional hazards regression. Students utilize R software to implement these methods on authentic datasets. Additionally, a collaborative project entails predictive modeling for a substantial dataset, fostering practical application skills.

Introduction to Databases & Information Retrieval

This course explores the fundamental principles of databases and techniques for effective information retrieval. Covering database design, query optimization, and search algorithms, it equips students with essential skills for managing and extracting valuable insights from vast datasets in machine learning applications.

Data Visualization

This course equips future analytics and data science professionals with practical skills to effectively visualize data and craft compelling narratives. Covering visualization rules, psychology, and storytelling, students learn to create powerful data stories and when to innovate within the visualization process.

Upon successfully completing this program, students will acquire the following skills and learning outcomes:

•  Proficiency in machine learning algorithms
•  Data preprocessing and feature engineering skills
•  Model evaluation and optimization techniques
•  Advanced statistical and mathematical concepts application
•  Programming proficiency in languages like Python or R
•  Understanding of deep learning principles
•  Practical experience in real-world machine learning projects
•  Ethical considerations in machine learning applications

Masters in Machine Learning Online Program Degree Types

Masters in Machine Learning Online is mainly offered as a Masters of Science in Machine Learning – MS in Machine Learning. Or it can be offered as a subset of an MS in Data Science. Some universities offer Machine Learning as a concentration within an MS in Computer Science.

Artificial Intelligence and Machine Learning

Machine Learning runs on the concept that machines should be able to learn from the existing data and adapt through experience. Whereas Artificial Intelligence enables a machine to analyze and perform actionable tasks – similar to the human being. The following are the prime differences between AI and ML:

Artificial Intelligence	Machine Learning
Creating intelligent machines to mimic human behavior Developing machines that can solve complex problems Machine Learning and Discipline Learning are subsets of AI	Automatically learning from the previous data to give accurate output With no explicit program, it automatically learns from previous data Discipline Learning is a subset of A

Some universities offer online masters in AI and ML. Here are some universities that offer these dual programs:

• Colorado State University-Global Campus²
• The University of Texas at Austin³
• Drexel University⁴

Concentrations and Related Programs

Machine Learning is a subset of Artificial Intelligence and a part of Data Science. Data Science is a broader aspect that blends the concepts of statistics, technical skills, and business vision to predict results. Machine Learning is based on Data Science for focusing and calculating algorithms and statistics of the given data to analyze the situation. Combining Machine Learning and the following programs will enhance skills and bring better career opportunities.

Area of Focus	Description	Careers This Concentration Prepares For
Data Analytics	This specialization focuses on extracting actionable insights from diverse datasets. Students learn advanced techniques, statistical methods, and data visualization to make informed decisions and drive business value through effective analysis.	Data AnalystsBusiness Intelligence AnalystsData Engineers
Artificial Intelligence (AI)	This specialization explores advanced AI algorithms, machine learning, and deep learning. Students acquire expertise in developing intelligent systems, natural language processing, computer vision, and autonomous agents, preparing them for impactful roles in AI research and application development.	Machine Learning EngineersAI Research ScientistsNatural Language Processing Engineers
Data Science	This specialization focuses on advanced analytics, statistical modeling, and machine learning techniques. Students gain expertise in extracting meaningful insights from complex data sets, making data-driven decisions, and contributing to the growing field of data science innovation.	Data ScientistsStatisticiansData Architects
Predictive Analytics	This specialization explores advanced data modeling and machine learning techniques. Students acquire skills to analyze historical data, build predictive models, and make informed forecasts, enabling them to contribute to data-driven decision-making and strategic planning in various industries.	Predictive ModelersRisk AnalystsOperations Analysts
Deep Learning	This specialization offers an in-depth exploration of advanced neural network techniques. Students explore complex topics such as convolutional and recurrent neural networks, reinforcement learning, and natural language processing, equipping them with specialized skills for cutting-edge applications in machine learning.	Deep Learning EngineersNeural Network DevelopersComputer Vision Specialists

Online Master’s in Machine Learning Program Length

A professional seeking a Machine Learning course may be interested in the duration it takes to complete the program. Many factors go into determining the length of the programs, given that there are several parameters like full-time, part-time, how many credits per semester, background, and more. Most students will graduate from a program for a master’s in machine learning in about two years.

Accelerated Machine Learning Programs

Accelerated master’s programs in Machine Learning offer a streamlined path for students to complete their degree efficiently. These programs cater to motivated and dedicated students aiming to expedite their studies and graduate earlier. To expedite their progress, students can accelerate their program by opting for an increased course load, including summer or intensive courses. Additionally, they may be able to transfer credits from prior coursework or relevant professional experiences, reducing the overall time needed to complete the Machine Learning degree.

Universities offering masters in machine learning online are seeing a lot of footprints and applicants coming by to enroll in the online program. But, during this process, many forget to check the admission requirements before applying to delay the enrolment process. So, it is better to have a checklist of admission requirements and prepare accordingly:

• Bachelor’s Degree: A completed undergraduate degree in a related field such as computer science, engineering, mathematics, or a relevant discipline.
• Academic Transcripts: Submission of official academic transcripts from all previous institutions attended.
• Letters of Recommendation: Typically, two or more letters of recommendation attesting to the applicant’s academic and/or professional qualifications.
• Statement of Purpose: A written statement outlining the applicant’s academic and career goals and reasons for pursuing a Master’s in Machine Learning.
• Resume/CV: A detailed resume or curriculum vitae highlighting relevant work experience, projects, and skills.
• Standardized Tests: Some programs may require GRE scores. Check specific program requirements.
• Programming Proficiency: Demonstrated proficiency in programming languages commonly used in machine learning, such as Python or R.
• Work Experience (Optional): Some programs may consider relevant work experience a factor in admission.
• Interview: Some programs may require an interview as part of the admission process.

The (Graduate Record Examination (GRE) and Graduate Management Admission Test (GMAT) are commonly used standardized tests for graduate program admission. While many U.S. schools often require these scores, some online master’s programs in Machine Learning may not demand a GRE or GMAT. Waiving this requirement recognizes that standardized test scores may not predict an applicant’s success consistently. Colleges may instead emphasize factors like academic background, research experience, statement of purpose, letters of recommendation, and interviews. Prospective students pursuing an online master’s in Machine Learning should contact their chosen universities’ Machine Learning departments to explore programs without GRE requirements or options for obtaining a waiver.

Accreditation ensures that a university meets specific educational standards, guaranteeing the quality of its programs. For students, choosing an accredited university for a Master’s in Machine Learning is crucial because accreditation affirms the program’s legitimacy, faculty competence, and academic resources. Besides, accredited programs are recognized and valued by potential employers. The Artificial Intelligence Board of America (ARTiBA)⁵ (ARTiBA) is one of the primary accreditation bodies that confirms if the university/college follows the quality benchmark in learning, teaching, and researching Artificial Intelligence and Machine Learning

Universities can also be accredited nationally or by one of six regional accrediting agencies:

• New England Commission of Higher Education (NECHE)
• Middle States Commission on Higher Education (MSCHE)
• Higher Learning Commission (HLC)
• Southern Association of Colleges and Schools Commission on Colleges (SACSCOC)
• Northwest Commission on Colleges and Universities (NWCCU)
• Western Association of Schools and Colleges (WASC) Senior College and University Commission (WSCUC)

Read more about accreditation in the Accreditation Guide.

In the field of machine learning, students can register for free courses to deepen their comprehension of the field. Some of these free courses provide insights into professional practices within the domain. Upon successful completion, participants may be awarded certificates of completion. It’s crucial to recognize that while these certificates acknowledge completion, they may not carry the same weight as accredited courses. Below are examples of free machine learning courses:

How to Pay for Machine Learning Programs?

Machine Learning has become the favorite subject of professionals and students alike, and many look forward to enrolling in Machine Learning programs according to each one’s convenience. However, the primary question students would always have is the process of paying for the program. Many universities offer some kind of financial assistance to students. There are  many other ways to pay for higher education, like – employer reimbursed tuition, student loans, fellowships, grants, etc.

Read more about financial assistance in the best finance guide.

Scholarships for Machine Learning Students

Apart from student loans or other financial aid forms, it would greatly help if the program is conferred on a scholarship. The grant of an award encourages an enthusiastic and deserving candidate in enrolling for the Master’s program and pursue it comfortably. Here is a list of some scholarship awards that are available for machine learning students, which include some fellowships that will bolster student’s experience:

The concepts of Machine Learning and AI are penetrating the markets at an unprecedented speed. Today, most companies looking for a thriving business are slowly transforming by adopting Machine Learning in one form or another. According to Indeed report 2019¹², Machine Learning Engineer stands first (#1) in the list of The Best jobs in the US, showing a 344% growth with a median salary of $146,085 per year. Here are other career choices that one can make by enrolling in a Master’s in Machine Learning:

Job Title	Average Salary	Job Growth (up to 2032)	Job Description
Machine Learning Engineer13	$123,151	No Data Available	Building and managing programs for machine learning projects
Data Scientist14	$99,606	No Data Available	Collect, analyze, and interpret large complex datasets by leveraging both machine learning and predictive analytics
Business Intelligence Developer15	$86,165	No Data Available	Analyze complex datasets to identify business and market trends
Machine Learning Researcher16	$144,871	No Data Available	A researcher in machine learning develops and implements contemporary methods such as artificial intelligence, statistical techniques, and various algorithms (clustering, classification, deep learning, transfer learning, adversarial learning, active learning, continuous learning, explanation learning, etc.) to extract patterns and models from industrial data. This involves collaborating with senior researchers and multidisciplinary teams to address supervised and unsupervised learning challenges within an applied research setting.

Certification serves as a formal acknowledgment of one’s skills meeting the necessary standards, while licensing is an official permit granted by an authorized agency, confirming eligibility to utilize one’s skill set for personal or professional purposes. Both certification and licensing empower individuals to apply their expertise effectively. Several agencies provide machine learning certification and licensing options. Below is a list of reputable machine learning certification  programs:

Associations, communities, conferences, and exhibitions provide opportunities to network with peers. Although individuals cannot be linked in a commonplace, a shared interest (Machine Learning) keeps them associated with each other. These channels assist both professionals and students in staying updated on global changes in Machine Learning concepts. Here are some associations/communities formed for Machine Learning enthusiasts and followers:

• International Machine Learning Society (IMLS) : The IMLS is a non-profit organization whose main aim is to foster machine learning research and whose main activity is coordinating the annual International Conference on Machine Learning (ICML).
• INFORMS : INFORMS is the leading international association for operations research and analytics professionals.
• Partnership on AI : The Partnership on AI conducts research, facilitates discussions, shares insights, offers thought leadership, collaborates with relevant third parties, addresses public and media inquiries, and develops educational material to enhance the comprehension of AI technologies. This includes machine perception, learning, and automated reasoning.
• Security Industry Association (SIA) : SIA advocates for non-regulatory approaches that promote AI research and development, empowering security professionals to leverage new capabilities for enhanced effectiveness in security and life safety systems.