For those interested in obtaining skills in the artificial intelligence (AI) field, one option is to pursue an AI certification.
AI certification programs provide an overview of the major areas in artificial intelligence and a test to show competence. For employers, an AI certificate provides third-party ratification of a candidate’s basic knowledge and skills related to AI development.
Most curriculums focus on the programming and math behind the fundamental topics within AI, with a heavy emphasis on machine learning (ML), deep learning (DL), natural language processing (NLP), and their practical applications.
Certificate programs vary in the number of courses required to obtain certification, experience required to participate, prerequisites, and specific topics covered. These differences may lead to some confusion about certificate types, so those who obtain a certificate should be prepared to explain what it covers.
Artificial intelligence education currently lacks standardization, but standards for AI definitions, ethics, and education have been proposed by the European Union, Institute of Electrical and Electronics Engineers, and International Organization for Standardization.
See more: Artificial Intelligence Market
Artificial Intelligence Certifications Today
To be successful in studying AI, students typically need a fundamental knowledge in:
- Python programming: most assignments will be to write programs in this language.
- College calculus and linear algebra: especially multivariable derivatives, matrix notation, vector notation, and common operations.
- Probability theory: basic distributions, basic functions, and fundamental definitions, such as expectation, independence, and dependence.
Organizations that certify in artificial intelligence fall into four main categories: vendors, technology associations, universities, and education platforms. Each backs the credibility for their credentials in different ways and certifies different aspects of AI knowledge.
Specialty certificates for specific platforms — such as Microsoft Azure, Google Cloud, and IBM Cloud — or for AI subcategories, such as machine learning, are available from several major AI companies.
See more: Machine Learning (ML) Certifications
Technology associations such as the Artificial Intelligence Board of America (ARTiBA) and the United States Artificial Intelligence Institute (USAII) do not teach classes. Instead, they offer study guides and tests that allow those with a basic understanding of AI to pass a test and obtain a credential.
Many universities offer degrees and certifications in AI such as Stanford, Massachusetts Institute of Technology (MIT), Northwestern University, University of Oxford, University of California Berkeley, The University of Texas at Austin, and more. These certificates cost significantly more than technology association certificates, but they also offer classes, student networking, and access to professors.
AI certifications are available at many online education platforms, such as Coursera, edX, Google, LinkedIn Learning, Udacity, and Udemy. The courses are run by various organizations. For example, MIT runs a course on Emeritus.org, Great Learning offers a course run by the University of Texas, and Coursera features certificates by IBM, Stanford, and CertNexus.
Artificial Intelligence Certificate Programs
The AIE certification by the Artificial Intelligence Board of America requires candidates to take an exam that costs $550. The fee covers the exam, certificate, and course study materials. To qualify to take the test, candidates need to have a college degree and work experience. The test covers ML, DL, DLP, regression, and types of AI learning: unsupervised, reinforced, and supervised.
The Coursera education platform offers about 25 AI courses that also provide a professional certificate. The prerequisites, level of student experience, and costs vary. Some courses offer a broad certification, and others offer a narrow certification. Offerings include courses such as:
- CertNexus Certified Artificial Intelligence Practitioner: Vendor-independent certification courses for the CertNexus certification exam. Designed for intermediate levels of understanding in AI, the coursework covers business applications, AI workflow, and ML/DL algorithms. The exam costs $250, with course cost and study materials extra.
- Deep Learning.AI TensorFlow Developer: This intermediate-level certificate course is offered by DeepLearning.AI, an education company focused on AI education. This course, and a similar course on Udacity, prepares students to take the Google TensorFlow Certificate Exam, which is $100. The course recommends python programming skills, ML or DL knowledge, and a mathematical background. The course covers how to build scalable AI applications through programming assignments to build and train neural networks, improve performance, NLP and text processing.
- IBM Data Science: This beginner-level program does not require any programming experience and teaches open source tools for Python, databases, SQL, data visualization and analysis, statistical analysis, predictive modeling, and machine learning algorithms. The course uses the IBM Cloud and incorporates data science tools and data sets.
Massachusetts Institute of Technology
MIT offers at least two AI certificates. The AI Product Design Certificate through Emeritus.org is equivalent to a single class in scope and cost. The broader and more expensive Professional Education Certificate is offered directly through the university.
The professional education certificate costs $325 for the application and between $2,500 and $5,500 per in-person class. To qualify to take the courses, students must have at least three years of professional experience and a bachelor’s degree in a technical field. Students must take at least the two core classes on ML and then complete a total of 16 days worth of electives on the ethics of AI, ML modeling and optimization, reinforcement learning, deep learning, health care AI, and more. At completion, students earn a certificate, continuing education units, and other benefits. The courses teach best practices, key concepts, algorithms, and practical applications. They also help students understand costs and performance hurdles in predictive modeling.
This specialty certificate from MIT at Emeritus.org costs $2,600 for the eight-week course that covers the foundations of AI, ML, DL, solving problems, intelligent human interaction and integration, and AI research and practice.
Stanford University: Artificial Intelligence Professional Program Certificate
The Stanford AI certificate program costs $4,785 for three courses, and students earn 10 continuing education units (CEU), the certificate, and other benefits. The course covers an introduction to machine learning and both theoretical and project-based learning in natural language processing and understanding.
USAII offers three AI certification programs:
- Certified AI Engineer: Designed for students or professionals with limited experience, this program costs $581 and includes all of the study books, e-learning materials, the exam and certificate. Applicants need to have a college degree and experience in programming to qualify to take the test. The test covers the basics of AI, ML, DL, NLP, reinforcement learning, and more.
- Certified Artificial Intelligence Consultant: This program costs $689 and includes all of the study books, e-learning materials, the exam and certificate. Applicants need to have a college degree and between two and six years of work experience or programming to qualify to take the test. The test covers the business applications of AI, ML, DL, NLP, robotics, economics, trends, managing analytics teams, and more.
- Certified AI Scientist: Designed for experienced working professionals, this program costs $793 and includes all of the study books, e-learning materials, the exam and certificate. Applicants need to have a college degree and at least four years of work experience or programming to qualify to take the test. The test covers in-depth concepts in AI, ML, DL, NLP, computer vision, generative adversarial networks, reinforcement learning, and more.