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10 top artificial intelligence certifications and courses for 2021

Numerous AI certifications and courses cover the basics and applications of artificial intelligence, so we narrowed the field to 10 of the more diverse and comprehensive programs.

Artificial intelligence is on track to be the key technology enabling business transformation and allowing companies to be more competitive. In fact, by 2030, AI could contribute up to $15.7 trillion to the global economy, according to PwC.

AI can help businesses be more productive by automating processes, including using robots and autonomous vehicles, and augmenting their existing workforces with AI technologies like assisted and augmented intelligence.

The majority of organizations are working to implement AI in their processes and products. Companies are using AI in numerous business applications, including finance, healthcare, smart home devices, retail, fraud detection and security surveillance.

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.

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.

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

Key elements: This 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 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 (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.

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5. AI For Everyone by Andrew Ng (Coursera)

Key elements: This course is mainly non-technical 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 (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.

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 (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 with no previous coding experience using Python, 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 (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 multi-armed 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.

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

Key elements: There are nine short courses in this learning path presented by industry experts to help individuals master the foundations and future directions of AI and machine learning and make more informed decisions and contributions in their work environments. Participants learn how leading companies are using AI and machine learning to change the way they do business and how the next generation of thinking about AI is addressing issues of accountability, security and explainability. Students will earn a certificate of completion from LinkedIn Learning after completing the following nine courses:

  • AI Accountability Essential Training. See why it's absolutely critical for AI-related data science work to be transparent, easily explained, accountable and ethical in its design and execution.
  • Artificial Intelligence Foundations: Machine Learning. Learn how to use machine learning to identify patterns in data and make better business decisions.
  • Artificial Intelligence Foundations: Thinking Machines. Examine the different approaches to AI, including machine learning and deep learning, strong and weak AI, and practical uses for new AI-enhanced technologies.
  • Artificial Intelligence Foundations: Neural Networks. Learn the key concepts behind artificial neural networks and how to configure a neural network and find patterns in massive amounts of data.
  • Cognitive Technologies: The Real Opportunities for Business. Learn about the benefits and business value of cognitive technologies, such as AI and robotics, and how they affect businesses.
  • AI the LinkedIn Way: A Conversation with Deepak Agarwal. LinkedIn's vice president of AI answers questions about AI's role at LinkedIn, careers in artificial intelligence and AI's future.
  • Artificial Intelligence for Project Managers. Learn about AI's impact on project management as well as how to harness the power of AI to streamline workflows, prepare for future changes and stay ahead of the curve.
  • Learning XAI: Explainable Artificial Intelligence. See how explainable artificial intelligence works and affects businesses and projects related to data science.
  • Artificial Intelligence for Cybersecurity. Learn how to use AI to solve complex problems in information security and explore the risks of using AI for security.

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