10 top artificial intelligence certifications and courses for 2023

Why AI certifications are important

Learning about and understanding artificial intelligence can set individuals on the path to promising careers in AI. A great way for people to immerse themselves in the technology is by taking AI courses and earning certifications in the discipline. Certifications enable individuals to succeed in a future powered by artificial intelligence and be viewed by potential employers as subject matter experts in the field of AI technologies.

10 of the best AI certifications and courses

1. Artificial Intelligence Graduate Program by Stanford University School of Engineering

Key elements: This graduate certificate program covers the principles and technologies that form the foundation of AI, including logic, probabilistic models, machine learning, robotics, natural language processing and knowledge representation. Learn how machines can engage in problem-solving, reasoning, learning and interaction and how to design, test and implement algorithms.

To complete the Artificial Intelligence Graduate Program, you must complete one required course and three elective courses. You must receive a 3.0 grade or better in each course in order to continue taking courses via the Non-Degree Option Program.

Prerequisites: Bachelor's degree with a minimum 3.0 grade point average; mastery of the prerequisite subject matter, including statistics and probability, linear algebra and calculus; and experience programming in C/C++, Java, Python or other similar languages. Each course may have individual prerequisites.

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2. Designing and Building AI Products and Services by MIT xPro

Key elements: This eight-week certificate program covers the design principles and applications of AI across various industries. Learn about the four stages of AI-based product design, the fundamentals of machine and deep learning algorithms and how to apply the insights to solve practical problems. Students can create an AI-based product proposal, which they can present to their internal stakeholders and investors.

Prerequisites: UI/UX designers, technical product managers, technology professionals and consultants, entrepreneurs and AI startup founders.

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3. Artificial Intelligence: Business Strategies and Applications by UC Berkeley Executive Education and Emeritus

Key elements: This certificate program is targeted at managers leading AI teams, rather than teaching the how-tos of AI development. It introduces the basic applications of AI to those in business, covers AI's current capabilities, applications, potential and pitfalls, and explores the effects of automation, machine learning, deep learning, neural networks, computer vision and robotics. Learn how to build an AI team and organize and manage successful AI application projects. Also study the technology aspects of AI to communicate effectively with technical teams and colleagues.

Prerequisites: C-suite executives, senior managers and heads of business functions, data scientists and analysts, and mid-career AI professionals.

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4. IBM Applied AI Professional Certificate (via Coursera)

Key elements: This beginner-level AI certification course will help students do the following:

• understand the definition of artificial intelligence, its applications, use cases and terms such as machine learning, deep learning concepts and neural networks;
• build AI-powered tools using IBM Watson AI services, APIs and Python with minimal coding;
• create virtual assistants and AI chatbots without programming and deploy them on websites; and
• apply computer vision techniques using Python, OpenCV and Watson, develop custom image classification models and deploy them in the cloud.

Prerequisites: Open to everyone with both technical and nontechnical backgrounds. The final two courses require some knowledge of Python to build and deploy AI applications. For learners without any programming background, an introductory Python course is included.

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5. AI for Everyone by Andrew Ng (via Coursera)

Key elements: This course is mainly nontechnical and covers the meaning of common AI terms, including neural networks, machine learning, deep learning and data science. Students also learn the following:

• what AI can and can't do;
• how to uncover opportunities to apply AI to problems in their companies;
• what it feels like to build data science and machine learning projects;
• how to work with AI teams and build AI strategies in their organizations; and
• how to handle ethical and societal discussions surrounding AI.

Prerequisites: Open to everyone, regardless of experience.

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6. Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning (via Coursera)

Key elements: This four-course deeplearning.ai certificate program covers best practices for using TensorFlow, an open source machine learning framework. Students will also learn how to create a basic neural network in TensorFlow, train neural networks for computer vision applications and use convolutions to improve their neural networks.

This is one of four courses that are a part of the DeepLearning.AI TensorFlow Developer Professional Certificate.

Prerequisites: Software developers who want to build scalable AI-powered algorithms. High school-level math and experience with Python coding are required. Prior machine learning or deep learning knowledge is helpful but not required.

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7. Artificial Intelligence A-Z: Learn How to Build an AI (via Udemy)

Key elements: This course covers key AI concepts and intuition training to quickly get up to speed with all things AI, including how to start building AI using Python with no previous coding experience, how to code self-improving AI, merge AI with OpenAI Gym toolkit and optimize AI to reach its maximum potential in the real world. Students will do the following:

• learn how to make a virtual self-driving car;
• create an AI to beat games;
• solve real-world problems with AI;
• master AI models; and
• study Q-learning, deep Q-learning, deep convolutional Q-learning and A3C reinforcement learning algorithm.

Prerequisites: Anyone interested in AI, machine learning or deep learning. High school math and basic Python knowledge, but no previous coding experience required.

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8. Artificial Intelligence: Reinforcement Learning in Python (via Udemy)

Key elements: This course covers how to apply gradient-based supervised machine learning models to reinforcement learning, implement 17 different reinforcement learning algorithms and use OpenAI Gym toolkit with zero code changes. The following topics are also covered:

• the relationship between reinforcement learning and psychology;
• the multiarmed bandit problem and explore-exploit dilemma;
• Markov decision discrete-time stochastic control processes;
• methods to calculate means and moving averages and their relationship to stochastic gradient descent; and
• approximation methods, such as how to plug a deep neural network or other differentiable model into a reinforcement learning algorithm.

Prerequisites: Calculus (derivatives), probability/Markov models, Numpy coding, Matplotlib visualizations in Python, experience with supervised machine learning methods, linear regression, gradient descent and good object-oriented programming skills. The course is open to students and professionals who want to learn about AI, data science, machine learning and deep learning.

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9. Artificial Intelligence Engineer (AIE) Certification Process by the Artificial Intelligence Board of America (ARTiBA)

Key elements: The ARTiBA certification exams compose a three-track AI learning deck that contains specialized resources for skill development and job-ready capabilities to help credentialed professionals move into senior positions as individual contributors or team managers. The AIE curriculum covers every concept of machine learning, regression, supervised learning, unsupervised learning, reinforced learning, neural networks, natural language processing, cognitive computing and deep learning.

Prerequisites: Students and professionals with different levels of experience and formal education, including associate (AIE track 1), bachelor's (AIE track 2) and master's (AIE track 3) degrees. Track 1 requires a minimum of two years of work history in any of the computing subfunctions. Tracks 2 and 3 say experience is not mandatory but good understanding of programming is essential.

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10. Master the Fundamentals of AI and Machine Learning (via LinkedIn Learning)

Key elements: There are 10 short courses in this learning path presented by industry experts. They aim to help individuals master the foundations and future directions of AI and machine learning and make more educated decisions and contributions in their organizations. Participants learn how leading companies are using AI and machine learning to alter how they do business, as well as insight into addressing future ideas regarding issues of accountability, security and clarity in AI. Students will earn a certificate of completion from LinkedIn Learning after completing the following 10 courses:

• AI Accountability Essential Training
• Artificial Intelligence Foundations: Machine Learning
• Artificial Intelligence Foundations: Thinking Machines
• Artificial Intelligence Foundations: Neural Networks
• Cognitive Technologies: The Real Opportunities for Business
• AI Algorithms for Gaming
• AI The LinkedIn Way: A Conversation with Deepak Agarwal
• Artificial Intelligence for Project Managers
• Learning XAI: Explainable Artificial Intelligence
• Artificial Intelligence for Cybersecurity

Prerequisites: Open to everyone, regardless of experience.

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