With numerous AI certifications and courses covering the fundamentals and applications of artificial intelligence, we've narrowed the field down to 10 programs that are more diverse and comprehensive.
Artificial intelligence is set to become a key technology that enables business transformation and enables companies to improve their competitiveness. IDC's 2022 study predicts that the overall AI software market will reach $791.5 billion in revenue in 2025 at a compound annual growth rate of 18.4%.
AI can help companies increase productivity by automating processes, such as using robots and self-driving cars, and by augmenting existing workforces with AI technologies such as assistive and augmented intelligence. Most organizations are working on implementing AI into their processes and products. Companies are using AI in numerous business applications, including finance, healthcare, smart home devices, retail, fraud detection, and security monitoring.
Why are AI certifications important?
AI certification is important for the following reasons:
- Learning and understanding about artificial intelligence can put individuals on the path to a promising career in the AI field.
- Achieving a prestigious AI certification can help you stand out from your competitors and show that you have the skills that employers want and need.
- The AI field is constantly changing, and keeping up with the pace of change can be difficult. Certifications tell employers that you are up to date with the latest developments in the field.
- Career sites all point out that professionals with AI certifications can earn more than those without certifications.
10 Best AI Certifications and Courses
1. Graduate Certificate in Artificial Intelligence from Stanford University School of Engineering
Important elements: This graduate certificate program covers the principles and technologies that form the basis of AI, including logic, probabilistic models, machine learning, robotics, natural language processing, and knowledge representation. Learn how machines can problem solve, reason, learn, and interact, and how to design, test, and implement algorithms.
To complete a graduate certificate in artificial intelligence, you must complete one or two prerequisite courses and two or three elective courses. She must earn a grade of 3.0 or higher in each subject to continue in the non-degree student option program.
Prerequisites: Applicants must have a bachelor's degree with a grade point average of 3.0 or higher and have college-level calculus and linear algebra with a good understanding of multivariate derivatives, matrix/vector notation and operations. is required. Knowledge of probability theory and basic probability distributions is required. Programming experience is also required, including familiarity with Linux command-line workflows, Java/JavaScript, C/C++ Python or similar languages. Each course may have individual prerequisites.
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2. Design and build AI products and services with MIT xPro
Important elements: This 8-week certification program covers AI design principles and applications across a variety of industries. Learn about his four stages of AI-based product design, the basics of machine learning and deep learning algorithms, and how to apply the insights to solve practical problems. Students can create AI-based product proposals and present them to internal stakeholders and investors.
Students can learn to apply machine learning techniques to real-world problems, design intelligent human-machine interfaces, and evaluate AI opportunities in a variety of fields such as healthcare and education. Students can use the AI Design Process Model to design and build an outline of an AI product or process.
Prerequisites: The program is primarily aimed at UI/UX designers, technical product managers, technology professionals and consultants, entrepreneurs, and founders of AI startups.
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3. Artificial Intelligence: Business Strategy and Applications by Professor of Executive Education and Emeritus, University of California, Berkeley
Important elements: Rather than teaching how-tos for AI development, this certification program is aimed at senior leaders and managers who lead AI teams who are integrating AI into their organizations. Introduces basic applications of AI for business stakeholders. Learn about AI's current capabilities, applications, opportunities, and pitfalls. We also explore the effects of automation, machine learning, deep learning, neural networks, computer vision, and robotics. In this course, you will learn how to build an AI team, organize and manage a successful AI application project, and learn the technology aspects of AI to communicate effectively with your technical team and colleagues.
Prerequisites: The program is primarily aimed at executives, senior managers and business unit heads, data scientists and analysts, and mid-career AI professionals.
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4. IBM Applied AI Professional Certificate (via Coursera)
Important elements: This entry-level AI certification course helps students:
- Understand the definition of artificial intelligence, its applications, use cases, machine learning, deep learning concepts, neural networks, and other terms.
- Build AI-powered tools with minimal coding using IBM Watson AI services, APIs, and Python.
- Build virtual assistants and AI chatbots and deploy them to your website without programming.
- Apply computer vision techniques using Python, OpenCV, and Watson.
- Develop a custom image classification model and deploy it to the cloud.
Prerequisites: This series is open to everyone with both technical and non-technical backgrounds, although the last two courses require some knowledge of Python to build and deploy AI applications. Includes an introductory Python course for learners with no programming experience.
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5. AI forEveryone (by Andrew Ng) (via Coursera)
Important elements: This course is primarily non-technical and covers the meaning of common AI terms such as neural networks, machine learning, deep learning, and data science. The course runs for approximately 10 hours on a flexible schedule. Students will also learn:
- What AI can and cannot do.
- How to find opportunities to apply AI to enterprise problems.
- What's it like to build a data science and machine learning project?
- How to work with your AI team to build an AI strategy within your organization.
- How to address the ethical and social debates surrounding AI.
Prerequisites: Anyone can participate, regardless of experience.
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6. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (via Coursera)
Important elements: This 4-course deeplearning.ai certification program is 18 hours long and covers best practices for using TensorFlow, an open source machine learning framework. Students will also learn how to create basic neural networks in TensorFlow, how to train neural networks for computer vision applications, and how to use convolution to improve neural networks.
This is one of four courses that are part of the DeepLearning.AI TensorFlow Developer Professional Certificate.
Prerequisites: This series is designed for software developers who want to build scalable AI-powered algorithms. High school level math and Python coding experience required. Previous knowledge of machine learning or deep learning is helpful but not required.
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7. Artificial Intelligence AZ 2023: Build 5 AI (Including ChatGPT)
Important elements: This course provides subscribers with a quick introduction to all things AI, including how to start building AI using Python with no coding experience, how to code self-improving AI, and how to integrate AI with OpenAI Gym. Learn about key AI concepts and intuitive training to get familiar with AI. Use toolkits to optimize your AI and unleash its full potential in the real world. Students will:
- Learn how to build a virtual self-driving car.
- Create an AI to win games.
- Solve real-world problems with AI.
- Master your AI models.
- Learn Q-learning, deep Q-learning, deep convolutional Q-learning, and A3C reinforcement learning algorithms.
Prerequisites: This certification is for people interested in AI, machine learning, and deep learning. High school math and basic knowledge of Python are required, but no previous coding experience is required.
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8. Artificial Intelligence: Reinforcement Learning in Python (via Udemy)
Important elements: This course teaches you how to apply gradient-based supervised machine learning models to reinforcement learning, how to understand reinforcement learning at a technical level, how to implement 17 different reinforcement learning algorithms, and how to use the OpenAI Gym toolkit without making any code changes. Learn how to use it. It also covers the following topics:
- The relationship between reinforcement learning and psychology.
- The multi-armed thief problem and the dilemma of exploration and exploitation.
- Markov decision discrete-time stochastic control process.
- How to calculate averages and moving averages, and their relationship to stochastic gradient descent.
- Approximation methods, such as how to incorporate deep neural networks and other differentiable models into reinforcement learning algorithms.
Prerequisites: Students will have knowledge of calculus (derivatives), stochastic/Markov models, Numpy coding, and Matplotlib visualization in Python, as well as strong skills in supervised machine learning techniques, linear regression, gradient descent, and object-oriented programming. Experience required. This course is aimed at 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 American Artificial Intelligence Board (ARTiBA)
Important elements: The ARTiBA Certification exam consists of a three-track AI learning deck that includes specialized resources for skill development and work-ready capabilities, allowing qualified professionals to work as individual contributors or team managers. It will help you get a senior position. The AIE curriculum covers all concepts in machine learning, regression, supervised learning, unsupervised learning, reinforcement learning, neural networks, natural language processing, cognitive computing, and deep learning.
Prerequisites: Students and professionals with varying levels of experience and formal education, including associate's degrees (AIE Track 1), bachelor's degrees (AIE Track 2), and master's degrees (AIE Track 3). Track 1 requires at least two years of work experience in one of the computing sub-functions. Experience with Tracks 2 and 3 notes is not required, but a good understanding of programming is.
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10. Master the basics of AI and machine learning (via LinkedIn Learning)
Important elements: This learning path features 10 short courses delivered by industry experts. These are aimed at helping individuals master the fundamentals and future directions of her AI and machine learning so that they can make more educated decisions and contributions within their organizations. Attendees will learn how leading companies are using AI and machine learning to change the way they do business, and gain insights to address future ideas around issues of accountability, security, and clarity in AI. Students receive a certificate of completion from LinkedIn Learning after completing the following 10 courses:
- Essential AI Accountability Training.
- Fundamentals of Artificial Intelligence: Machine Learning.
- Fundamentals of Artificial Intelligence: Thinking Machines.
- Fundamentals of Artificial Intelligence: Neural Networks.
- Cognitive technology: A real opportunity for business.
- AI algorithms for games.
- How to do AI LinkedIn: A conversation with Deepak Agarwal.
- Artificial intelligence for project managers.
- Learn XAI: Explainable Artificial Intelligence.
- Artificial intelligence for cybersecurity.
Prerequisites: Anyone can participate, regardless of experience.
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Andy Patrizio is a technology journalist with nearly 30 years of experience covering Silicon Valley, having worked as an employee or freelancer for a variety of publications including Network World, InfoWorld, Business Insider, Ars Technica, and InformationWeek. Ta. He is currently based in Southern California.
