20 Best Schools for an Online Master’s in Machine Learning

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Updated: April 29, 2024, Reading time: 30 minutes

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Artificial intelligence (AI) and machine learning, among its subsets, are making a tremendous impact on society and how we live! These include driverless vehicles, computer systems playing against human players, image recognition systems, and computer vision.

Machine learning systems can also be fun, such as the 2016 Google DeepMind AlphaGo AI win over a Go human grandmaster.

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Becoming a machine learning engineer demands hard work, including effective study habits and commitment to completion. But its numerous rewards, from gainful employment and fast career advancement to meaningful contributions to society, will always be well worth the hard work! Just choose the best machine learning program, preferably one recognized by the industry as a suitable preparation for the competitive job marketplace and workplace.

Grad School Center is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.

About Online Master’s in Machine Learning Programs

With an Online Master’s in Machine Learning degree, you have the advanced knowledge and skills to become a competent and confident professional in this field! You will also find plenty of gainful employment opportunities because AI and machine learning technologies are being utilized in diverse industries.

These technologies are used in industries like:

With the field growing at a fast rate of 38.1% (2022-2030), new applications are being discovered every day, too. This will translate to a growing demand for AI and machine learning specialists, including engineers.

Differences Between AI, Machine Learning, and Data Science

AI is a computer system’s capacity for mimicking specific human cognitive functions, such as learning new things and using these for problem-solving purposes. AI systems also use logic and math to mimic the reasoning processes used by humans.

Machine learning, a subset of AI applications, refers to utilizing mathematical models of data to assist a computer system in “learning” sans direct intervention or instruction. The computer system will continue to learn and improve itself based on its prior experience.

In other words, an “intelligent computer” utilizes AI to perform tasks on its own and think like a human. Machine learning is how said “intelligent computer” develops its evolving intelligence. 

According to Dr. Thomas Miller, data science is defined as a “combination of information technology, modeling, and business management.” Using a scientific approach, data scientists extract insights and meaning from data.

Machine learning is a process used by data scientists in their work, and thus, a graduate degree in data science implies proficiency in machine learning.

20 Best Schools with Online Master’s in Machine Learning Programs

Georgia Institute of Technology

Georgia Institute of Technology

In January 2014, Georgia Tech established a partnership with Udacity and AT&T in the launch of its Master of Science in Computer Science Specialization in Machine Learning program. This was the first of its kind from an accredited university that allowed students to earn a degree via massive online courses.

More than 10,000 students and alumni have formed a worldwide community that shares job information and professional support.

Students earn at least 30 credit hours to earn the degree. The curriculum consists of six hours of a core course, such as  Introduction to Graduate Algorithms or Computational Complexity Theory; 12-15 hours of specialization courses; and 15-18 hours of electives, such as Computer Vision and Big Data Systems & Analysis. Students take up to two courses during the fall or spring terms and one course in the summer.

Three years is the average time to completion, but Georgia Tech allows students to earn the degree within six years of the first matriculation. 

Columbia University

Columbia University

The Master of Science in Computer Science – Machine Learning program consists of 30 credits earned fully online. Students should maintain at least a 2.7 GPA to stay in the program and complete its academic requirements within five years from the start of the first course taken.

Aside from the breadth requirement, students must also complete two specialization courses: Machine Learning, Advanced Machine Learning, and Artificial Intelligence.

Students in the Machine Learning track develop their knowledge and skills of machine learning tools and techniques and their real-world issues and applications. Their studies also span a wide range of industries, from fraud detection and finance to bioinformatics and intelligent systems.

Most applicants possess an undergraduate degree in computer science, but candidates with bachelor’s degrees in other disciplines are encouraged to apply, and they must meet prerequisite requirements. These include at least four computer science courses, such as advanced programming and discrete math.

At least a 3.3 undergraduate GPA is required, as are three recommendation letters, a personal statement, a resume, and transcripts.

Duke University

Duke University

Graduates of Duke’s Master of Engineering Management – Data & Machine Learning Track program become respected data analysts, data scientists, and business intelligence analysts. Their core training includes the deployment, evaluation and management of machine learning and data analysis for improving business processes.

The two-year online program offers advanced courses in machine learning with applications in engineering management, data visualization, and marketing analytics and research.

The program has a strong entrepreneurial direction complemented by an intensive industry internship. Their training enables them to gather, interpret, and manage large data sets and then use them to discover information, guide decisions, and solve business issues.

Students learn from the best professors whose interdisciplinary research reflects Duke’s top-ranked status in research productivity.

Stevens Institute of Technology

Stevens Institute of Technology

Stevens’ Master of Science in Machine Learning program enables students to establish professional-level knowledge and skills in modern machine learning techniques. Their training ensures that they are qualified for supervisory positions in data science, data analytics, and machine learning by the time of their graduation.

Their career opportunities will also span sectors from intelligent systems, robotics, and natural language processing to bioinformatics, finance, and weather prediction.

Students also become more familiar with machine learning and deep learning paradigms, such as unsupervised learning, supervised learning, and reinforcement learning. Their theoretical foundation then underlines their ability to create real-world applications in their careers, whether in academia, business, or research.

Stevens offers full-time and part-time enrollment for its fully online program. Students may also choose from either the thesis or non-thesis track and choose from a wide range of internships. International students can also take advantage of the Curricular Practical Training program.

Drexel University

Drexel University Logo

With its interdisciplinary curriculum, Drexel’s online Master of Science in Artificial Intelligence & Machine Learning empowers current practitioners to see their field in a different light.

Students work with state-of-the-art systems and related tools and real datasets to build their sophisticated skill sets, which have immediate uses in their workplaces. The program has three core areas of study – data science and analytics, AI and machine learning applications, and theory of computation and algorithms.

Students earn 45 quarter credits from the College of Computing and Informatics. Drexel uses a quarter system, with each academic calendar consisting of four 10-week quarters. Students benefit from the faculty members’ extensive research experience in machine learning, data science, and computer vision, among other topics.

The average time-to-completion is two years for full-time matriculation, but students may also enroll part-time. The coursework includes two capstones in AI and machine learning.

Applicants with a strong computer science background will likely thrive in the program. If you don’t have it, you may first want to complete the Graduate Certificate in Computer Science program.

Colorado State University Global

Colorado State University-Global

Graduates of CSU Global’s online Master of Science in Artificial Intelligence & Machine Learning program become leaders of the next computing in their own right! Their training enabled them to gain the sophisticated skillsets vital to their success in AI, machine learning, and computer science.

This is a diverse program, too, with the student body consisting of current practitioners with relevant work experience and relative newbies to the field.

This is a 30-credit program with core courses like Principles of Programming, Leadership in Software Development, and Management for the Computer Science Professional. Students learn the effective use of the principles and practices of AI and machine learning in solving problems and developing software and solutions in business and industry.

The professors also train students in the latest deep learning libraries, such as Caffe, Keras, and Tensorflow, and proficiency in Python programming. Applicants with technical acumen and who meet the course prerequisites are given preferential treatment during admission.

University of Michigan Dearborn

University of Michigan Dearborn

UM, Dearborn’s Master of Science in Artificial Intelligence Machine Learning Concentration program provides students with rigorous training and comprehensive education in machine learning.

The 30-semester hours in the curriculum include core courses (12 credits) in artificial intelligence, computational learning, and software engineering. Students also complete three concentration courses (9 credits) in advanced AI, fuzzy systems, and stochastic processes, among other choices.

Elective courses, which can explore advanced AI concepts or key contextual areas, may be taken from other partner colleges aside from CECS. Students must maintain a” B” GPA for program retention purposes.

The program has three options – coursework, MS with a thesis, or MS with a project. The coursework option requires completing three electives outside of the machine learning track. The thesis option has a 6-credit master’s thesis requirement, with the student thesis prepared under an advisor’s supervision. The project option requires a 3-credit independent study project for completion.

Students may also earn the degree entirely online or in a hybrid form and choose between part-time and full-time matriculation.

Northeastern University

Northeastern University

Students in Northeastern’s Master of Professional Studies in Applied Machine Intelligence program focus their studies on human, data and technology literacy across diverse industries. Their training covers the practical applications of AI and machine learning in business ventures, healthcare and finance, and other industries.

Students enroll as part-time or full-time learners, but the quality of education, including its rigorous standards, is the same, and they can complete the program in 12-18 months, which is among the shortest duration on this list.

The multidisciplinary approach makes it possible for students to become proficient in the three literacies. First, technology literacy involves the effective management of complex computer systems.

Second, data literacy includes the development of business strategies that generate competitive advantage and innovation. Third, human literacy involves the development of transferable skills with applications in the field of computer science in general.

Students build a portfolio of work samples that they can use during their job applications. Their portfolio demonstrates their professional competencies in AI and machine learning, emphasizing the three literacies.

Georgia Southern University

Georgia Southern University

The Master of Science in Computer Science Machine Learning Specialization has a hybrid delivery format. It also offers three options – a standard master’s degree, provisional admittance, and an accelerated bachelor’s-to-master’s degree. Full-time students in the standard format can earn the degree in two years.

This 30-credit machine learning track combines exceptional quality of instruction, academic rigor, and hands-on experiential learning opportunities.

Most students have a strong computer science background, but there are also students without a computer science or other related degrees on provisional admission. All students, however, gain proficiency in the development of programs that can access data and use it for learning on their own.

There are two options offered – a thesis and a non-thesis option. Their coursework is similar to courses on Artificial Intelligence – Theory and Application, Database Systems Design – Theory and Application, and Algorithm Analysis and Data Structures.

The machine learning track has courses like Data Mining and choices in electives like Data Warehousing, Decision Support Systems, and Computational Intelligence.  

Contemporary Technology University

Contemporary Technology University

Students in CTU’s Master of Science in Computer Science Artificial Intelligence & Machine Learning program benefit from its  Study Now, Pay Later Model, meaning little to no student debts! Students don’t make tuition payments until they are hired, at which point they start $500/month tuition payments.

Without tuition concerns, students can focus on mastering AI and machine learning skills vital in the transportation, healthcare, and manufacturing sectors.

The sequential learning path means that a student must complete one course before gaining access to the succeeding course. Enrolling in and completing one course at a time means students have greater flexibility in their time management and can apply their learning in the next course.

Each of the four semesters has three courses, each lasting four weeks, except for the final semester, which is devoted to a capstone project.

This is a 30-credit program with a one-year time-to-completion. Skills learned include Python programming, machine learning, and data science.

University of North Dakota

UND’s online Master of Science (MS) in Applied Economics & Predictive Analytics program is an excellent way of getting into the machine learning business. Predictive analysis is used when machine learning is applied in business problem-solving. If you’re interested in a job involving data forecasting, data consulting, and data analytics, you should consider it for its outstanding quality of instruction, flexibility, and affordability.

Students earn 30 credits over the two-year program. The coursework starts with courses in econometrics, programming languages, and statistical analysis, which build technical skills. Students also learn data design structures, including cross-sectional data and time-series data, and machine learning approaches like clustering methods.

These hard skills are useful during the independent research study that every student must complete under the guidance of their faculty advisors and with the opportunity to study via the Institute of Policy and Business Analytics.

Students may apply for UND’s STEM Loan Forgiveness Program, too.

Johns Hopkins University

Johns Hopkins University

JHU’s reputation for academic excellence is evident in its online Master of Science in Artificial Intelligence program! Students understand AI’s history, current trends, future direction, and associated topics. Machine learning, robotics, and intelligent systems are among its focus areas, and these courses are designed to prepare students for successful careers as AI engineers.

There are ten courses – four core courses and six electives – with a five-year maximum completion period. Students also choose between the theoretical track and the applied track.

Both tracks encourage students to explore a wide range of AI areas, including machine learning, computer robotics, and image processing, through virtual classroom instruction and the Applied Physics Lab. 

Students customize their degrees according to their career goals, too. Graduates are proficient in the drivers, functions, and requirements of AI systems and processes, possess sophisticated skill sets in developing new AI features, and become leaders in the industry.

University of San Diego

University of California San Diego

USD’s Master of Science in Applied Artificial Intelligence program consists of 30 units that can be completed in 20 months of full-time study. There are five 14-week semesters, each consisting of two courses and each course lasting for seven weeks; each course is taken one at a time for greater flexibility in study time.

Students are also admitted during the spring, summer, and fall terms so that interested applicants can start at their convenience.

The program emphasizes real-world applications alongside privacy, ethics, and social and moral responsibility in designing and developing AI-enabled systems. Students learn the fundamentals and applications of machine learning, as well as probability, statistics, and applications for AI purposes.

There’s a capstone project requirement where students work in teams toward identifying AI problems, developing possible solutions, and evaluating results. Students also develop their respective portfolios that highlight their AI proficiency and experience to potential employers.

Penn State World Campus

Pennsylvania State University World Campus

Students in Penn State’s Master of Professional Studies in Artificial Intelligence program complete 33 credits toward their degree. Their main strengths are their competent ability to identify, prepare, and process large data sets and design and develop algorithms in AI and machine learning.

Students demonstrate robust multidisciplinary skill sets in AI and machine learning, given their comprehensive theoretical education and hands-on experiences.

The program entails 33 credit hours with courses in the 400, 500, and 800 levels. At least a 3.0 GPA needs to be maintained in the 21-credit coursework, 9-credit electives, and 3-credit integrative research course with a written paper.

In the capstone course, students must demonstrate their competency in the theories and applications of AI. The course titled Natural Language Processing, Machine Vision, and Deep Learning is at the core of the student’s learning experience.

Southern Methodist University

Southern Methodist University

SMU’s Lyle School of Engineering offers an online Master of Science in Computer Science with an Artificial Intelligence Specialization program that trains students in advanced AI and machine learning technologies.

Students are encouraged to make direct and immediate applications of these technologies in their current organizations, and their feedback can also be used in the program. Such direct application emphasizes that students gain valuable practical skills even while pursuing their degree.

The robust curriculum includes courses in Machine Learning and Neural Networks, Machine Learning in Python, and Computer Architecture.

Yeshiva University

Yeshiva University

The Master of Science (MS) in Artificial Intelligence program at Yeshiva consists of 36 credit hours with a fixed tuition rate for all students regardless of their completion time (1-2 years) or status (domestic or international). The online program allows part-time or full-time enrollment and offers evening courses.

Students work in small classes that enable expert faculty members to provide one-on-one mentorship. AI theory, applications, and technologies are at the heart of the program, and a strong sense of ethics complements the technical skills learned.

Students design and build a wide range of AI applications, including machine learning, assistive agents, and computer vision, develop AI algorithms, and translate AI research into real-world applications. Yeshiva emphasizes product and service development that can be used to create start-ups.

Students can apply for AI Fellowships and are eligible for the STEM-OPT program. Internships provide hands-on experiences and industry mentorship, and students can showcase their capstone projects during the annual conference.

University of Houston – Victoria

University of Houston Victoria

Students in the Master of Science in Computer Information Systems with a Concentration in Artificial Intelligence program become competent in creating neural networks through TensorFlow, a crucial machine learning skill.

Fluency in the use of Statistical Machine Translation and their related applications also become part of their skill sets, aside from software development and intelligent agent design, all of which are vital in solving real-world problems.

Program completion can be achieved in 16 months, but it depends on your course load. There are 36 credit hours in the curriculum spread as 15 credits for core courses, 18 credits for electives, and three credits for an integration course.

Data Science using Machine Learning, Deep Learning, and Computer Vision and Image Processing is among the electives, while the core courses are in Database Design, Network Design and Management, and Systems Analysis and Design.

University of Illinois Urbana-Champaign

UIUC’s online Master of Computer Science in Data Science program has a non-thesis format, meaning students only deal with courses relevant to data science and its topics.

Students earn 32 credit hours consisting of eight 4-credit courses, including machine learning, cloud computing, data visualization, and data mining. The time to completion can be anywhere between one and five years, so students have greater flexibility in their work-study-life balance.

Students receive didactic lectures via Coursera’s MOOC platform and, thus, proceed at their own pace. However, the faculty and teaching assistants at UIUC conduct the assessments based on university-compliant projects, assignments, and exams.

The combination of both results in earning university credits. Machine learning courses include Applied Machine Learning, Natural Language Processing, and Computational Photography. 

Applicants must be competent computer programmers, particularly in the R, C++, Python and Java programming languages, to be considered for admission.

Baylor University

Baylor University

In the 100% online Masters in Computer Science Data Science Concentration program, students earn up to 42 credits, including foundational courses. The courses last for 15 weeks and cover the tools, techniques and algorithms for big data management, such as machine learning, data management, and computer visualization. There are three intakes per year so that students can start at their convenience.

The Data Science Track courses include Applied Data Science, Cloud Computing, Data Visualization, and Data Mining and Analysis. Students explore the analytical methods in dealing with data science’s five Vs.; examine AWS, Azure and Google’s cloud environments; and learn parallel programming.

Applicants don’t have to possess a bachelor’s degree in computer science, engineering or physics, but these are preferred backgrounds. Proficiency in Python, Java, C# or C++ is also recommended.

Eastern University

Eastern University

The completely online Master of Science in Data Science program is the most affordable on this list, and it’s made even more affordable with a 20% alumni discount, military benefits and other forms of financial aid. Thanks to the 7-week, self-paced course design, new students are enrolled every seven weeks. Students earn the degree in as little as ten months, too.

The 30-credit program allows matriculation for six credits in every 7-week session, thus, the quick time-to-completion. Students gain workplace-ready specific skills, including proficiency in coding languages, machine learning, and DS-standard programming.

Eastern also offers a dual degree through the MBA/MS in Data Science program, which can be earned in 20 months. Students earn 60 credits for the dual degree, and its 100% online delivery makes it convenient for working professionals.

10 Highly Profitable Careers with a Graduate Degree in Machine Learning

The diverse range of rewarding careers for graduates with a master’s in machine learning or related fields is among the reasons for its increasing popularity.

These careers are in the public and private sectors, including state and federal government agencies, business, and healthcare, among other industries. Even the federal government has benefited from it, such as machine learning at the Bureau of Labor Statistics!

1. AI Specialists and Developers

Ranked #1 on LinkedIn’s 2020 Emerging Jobs Report, AI specialists and developers use a combination of computer science, software engineering, and AI concepts to design, develop, and deploy autonomous systems.

Many are also engaged in finding solutions for industry, business and research issues through text recognition, image recognition and natural language processing. Their work can lead to the streamlined automation of daily operations in diverse industries and organizations.

Facebook, Amazon, Accenture, Microsoft, Google, MoTekTechnologies, and NVIDIA are among the most popular employers hiring AI engineers and specialists. But, with more organizations integrating AI technologies into their systems, the demand is on the rise.

2. Machine Learning Engineers

With their exceptional proficiency in diverse programming languages, machine learning engineers design, develop, and build self-running software used to automate predictive models.

Their AI systems leverage large data sets in the development of algorithms with the capacity for learning that, in turn, contributes to their ability to make predictions and carry out more efficient operations.

3. Machine Learning Researchers

With their strong background in research, machine learning researchers design and test new models that can be used in real-world applications. Their research studies may also be used to improve business applications, processes, and operations, such as predictive analysis.

Their emphasis on theoretical research can also bridge the gaps between theory and practice, particularly in real-world manufacturing systems.  

4. Machine Learning Specialists

The discovery, design, and development of machine learning algorithms and other analytical methods for supporting new approaches in data processing are among the main duties of machine learning specialists.

Their job also includes the performance of exploratory data analysis, preparation and analysis of historical data to identify patterns, and delivery of technical support related to business development and program management activities.

5. Data Scientists

Stakeholders work with data scientists to understand their goals and how data can be applied to their achievements. Data scientists design algorithms and predictive models, data modeling processes, and data analysis for business-related purposes.

Other job titles include data engineer, data architect, and data analyst.

6. Algorithm Engineers

AI systems use specific algorithms to discover patterns within large data sets, and AI machines rely on new algorithms to learn and improve their functions. This is where algorithm engineers come in with their proficiency in writing new algorithms, testing them on AI machines, and determining their results.

7. Business Intelligence Developers

BI developers are engineers in their own right, with their main responsibility being the development and maintenance of business intelligence interfaces. The critical components in BI interfaces are interactive dashboards, data modeling, and data visualization.

BI developers aren’t the brains behind the codes but are the brains behind the designs and strategies of BI systems.

8. Computer Vision Engineers

Computer vision refers to how devices make sense of images, from the detection and comparison of images to identifying patterns and inconsistencies. Using the technology, computer vision engineers find solutions for real-world issues, such as workplace safety, railroad defects, and progress in physical therapy.

9. Software Developers

AI systems require software for effective and efficient operations, and software developers are behind these computer programs. Software developers design, write, and test software for diverse purposes and recommend updates for current software.

Their job also includes the use of machine learning in making software smarter and improving data analysis.

10. Computer and Information Research Scientists

AI and machine learning professionals may also take the generalist route of computer and information research scientists whose expertise is in high demand in business, medicine, and industry. Their responsibilities include collaborating with engineers and scientists to explore and resolve complex computing problems.

The unprecedented growth in the AI and machine learning landscape means that a master’s degree in these areas will open up job opportunities that a bachelor’s degree alone will likely not.

3 Reasons for Earning a Master’s in Machine Learning Degree

Colleges and universities spend more on STEM education than on education in the humanities and liberal arts, and the average tuition for STEM master’s degrees is proof of it.

However, graduates of STEM graduate degree programs have unparalleled opportunities for high-demand, high-paying jobs that make the cost of attendance and investments in time, energy, and effort worthwhile.

1. Huge Career Potential

Most master’s in machine learning degree programs have a generalist approach in their philosophy and program of study but provide enough detail for a fairly specialized skill set.

Students then gain advanced knowledge and skills that can be applied across various jobs, from machine learning engineers and robotics to software developers. Indeed, the number of entry-level positions you can be qualified for is aplenty, and thus, you won’t be an unemployed individual with a master’s degree!

While many entry-level AI and machine learning positions require only a bachelor’s degree from a regionally accredited institution, a master’s degree will put you ahead of the competition. A master’s degree is a testament to your strong commitment to advanced education and training and your traits, including perseverance, passion, and professionalism.

You will also be more competitive when it comes to supervisory and management positions with a master’s degree and relevant work experience. In the 21st century, there’s an increasing movement toward a master’s degree being the equivalent of a bachelor’s degree some decades ago.

2. Exceptional Earning Potential

AI and machine learning specialists, including engineers, researchers and analysts, are among the highest-paid professionals! Their STEM-heavy education and training, which are considered the most academically challenging, combined with their crucial importance in diverse industries, also contribute to their high demand.

On average, machine learning specialists earn $105,193 per year, according to Glassdoor, while machine learning engineers make $160,471 per year. Software developers earn good money, with a median salary of $124,200, while data scientists make an average of $103,500 annually.

The bottom line: You will likely recoup the costs of your graduate education in less than a year after earning it! You may even be able to graduate with little student debt since many schools offer generous financial aid, including assistantships and scholarships to online students.

3. Potential for Significant Positive Impact on Organizations

But it isn’t all about the monetary rewards either! You can positively impact your organization’s growth, whether it’s for a for-profit corporation or a non-profit venture. Your impact will come from both the application of your technical skills to improve its operations and your transferable skills in its corporate culture.

You may be a catalyst for change in your organization because of your machine learning-related contributions and your innovative and collaborative approach to problem-solving.

Of course, it isn’t all roses, either! There are disadvantages to pursuing a master’s degree in machine learning that you should be aware of before deciding.

Funding your studies is a significant concern unless you have substantial savings, a trust fund, or a generous sponsor. Even with scholarships and assistantships, you must factor in the cost of living and incidental expenses during your graduate studies. You may also be in the red if you’re pursuing full-time studies at the expense of a paying job.

Another thing is that getting adjusted to life as a graduate student will take some time. You may have difficulty maintaining a good work-life-studies balance, particularly if you’re a full-time student with a family to raise. Even returning to school can be a big adjustment for your family! 

So, should you pursue a degree in machine learning? Yes, you should consider it because the reasons outnumber the excuses! You will surely encounter challenges that will result in self-doubts, but you can work through these times and continue with your journey.

While there’s no guarantee of a successful career with a graduate degree, it’s an edge that you can leverage!

4 Ways a Master’s in AI and Machine Learning Degree Will Boost Your Business

Business leaders with the ability to leverage their AI and machine learning knowledge toward business growth already have a competitive edge! With the ever-changing landscape of business, which the coronavirus pandemic may have profoundly changed, their decision-making processes could use machine learning techniques, too.

1. Gain a Deeper Understanding of Business Growth

Students learn about and discuss the latest disruptive technologies related to AI and machine learning, many of which have huge potential applications in business.

Many applications are already in the market and used, but you may have little to no knowledge of them until you are exposed to them during your master’s studies. Once you gain a deeper understanding of machine learning applications in business, you’re better able to apply them to your business’s specific needs.

And there are plenty of business-specific machine learning applications that can be explored and used in your business! A few examples are real-time chatbot systems, decision support, demand pricing strategies, and operational efficiencies. Your advanced degree will also open your mind to the potential of data-based decision-making in fueling your business’ growth.

2. Strengthen Your Decision-making Skills

The art and science of business decision-making have a steep learning curve, but AI and machine learning can take up many tasks delegated to intelligent systems. You, the business leader, then have more time and energy for the matters that need full human attention, including the optimal use of data.

Students in these graduate programs strengthen their data-driven decision-making skills through machine learning, data visualization, and data analytics courses.

These courses focus on selecting and creating appropriate data models that are useful in predicting the outcomes of decisions. Understanding data-based insights with a business mindset is also valuable in effective decision-making.

3. Create Unique Business Value

Creativity and innovation in business may not be popular buzzwords, but these are among the in-demand soft skills business organizations look for! AI and machine learning are fields of study that encourage more innovation and creativity in finding new solutions to issues.

When used alongside your management skills, you can create unique business values that can spur unprecedented growth in your company.

There are also a few crucial steps in creating business value from machine learning. Start by creating a data fabric for your business, followed by fostering a laboratory environment with robust incentives for innovation.

Putting into successful operation pilots and scaling up for organization-wide adoption are the next logical steps.

4. Leverage Customer Data for Additional Business Value

Customers are the lifeblood of any business, and, as such, gaining a deeper understanding of their demographics and desires will add to business value.

You can leverage customer data by using AI and machine learning techniques, particularly in finding consumer behavior patterns. In doing so, marketing programs become more effective and efficient, from identifying target customers to offering promotions.

The bottom line: Customer acquisition and retention become easier with machine learning.

What are the typical courses in the master's degree programs to become a Machine Learning Engineer?

Frequently Asked Questions

What are the typical courses in the Master’s degree programs for becoming a Machine Learning Engineer?

While every program has its unique curriculum, several courses are common among these programs, including:

Depending on the program, students may also be required to submit a thesis and a capstone project. These requirements are designed to demonstrate the student’s proficiency in machine learning aspects.

What are the internship programs like?

While not all master’s degree programs require internships, these are crucial hands-on experiences that strengthen the students’ theoretical knowledge and practical skills learned during the didactic coursework. The program and students have distinct roles and responsibilities in programs with an internship component.

The program is responsible for identifying, creating, and disseminating the internship opportunities that students can apply for. Many programs have long-standing partnerships with diverse organizations, from business corporations to non-profit companies, for these internship opportunities.

If the program allows it, the students may also be required to look for internship opportunities. Tapping into your personal or professional networks is an excellent way of finding the best internships.

Students who are interns in third-party organizations must comply with the educational and professional standards outlined in the internship agreement.

What are the common prerequisites for admission to Machine Learning programs?

Due to the STEM-heavy curriculum, applicants to master’s in AI and machine learning programs must meet course prerequisites specific to every program. Discrete mathematics, statistics, probability, and a strong background in computer science and its related fields are common.

Most machine learning programs allow applicants to meet the prerequisites within a specific period, 12 months before starting the formal coursework. It’s also possible to earn a graduate certificate first in machine learning to establish a strong foundation and start the credit-bearing coursework.

Are Artificial Intelligence and Machine Learning difficult to study?

These are complex disciplines with a strong STEM focus and, thus, can be extremely challenging for individuals without the education, training and keen interest in science, technology and math.

But if you start at an early age, say, during your elementary years building a robust STEM foundation, AI and machine learning become more enjoyable and less difficult. 

What are the ways to study a Machine Learning specialization?

There are two ways that you can get started on machine learning. First, you can adopt the theory approach, where your skill sets are acquired through formal education and training. You earn a bachelor’s degree in computer science or other related fields and then pursue a master’s degree in machine learning.

Second, you can pursue the results first approach using a suite of AI and machine learning tutorials. You can generate results and add value to them quickly and adopt a specialist focus relevant to your project. You’re learning machine learning concepts outside of a formal and structured program.

Which one is the best depends on your learning style and preference! You can combine these two methods and expand your skill sets faster.

Key Takeaways

Additional Resources:

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