10 top artificial intelligence certifications and courses for 2025

Why AI certifications are important

AI certifications are important for the following reasons:

• Learning about and understanding artificial intelligence can set individuals on the path to promising careers in AI.
• A prestigious AI certification can set you apart from the competition and show employers that you have the skills they want and need.
• The AI field is constantly changing, and it can be a challenge to keep up with that pace of change. Certification tells an employer you are familiar with the latest developments in the field.
• AI demands advanced education. A National University examination of 15,000 job postings on Indeed.com found that nearly 80% of AI job openings require candidates to have a master's degree, while 60% demanded at least a bachelor's degree. Another 18% required a PhD, while only 8% would consider a high school diploma.

10 of the best AI certifications and courses

1. Artificial Intelligence Graduate Certificate 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 as well as how to design, test and implement algorithms.

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

Prerequisites. Applicants must have a bachelor's degree with a minimum 3.0 grade point average as well as college-level calculus and linear algebra credit, including a good understanding of multivariate derivatives and matrix/vector notation and operations. Familiarity with probability theory and basic probability distributions is necessary. Programming experience, including familiarity with Linux command-line workflows, Java/JavaScript, C/C++, Python or similar languages, is also required. Each course might have individual prerequisites.

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2. MIT's Professional Certificate Program in Machine Learning and Artificial Intelligence

Key elements. This certification program is operated like a traditional college course, running for 16 days and taught during the summer either online or at MIT's campus. Courses are taught by MIT's AI professors, among the best in the business. The program provides a well-rounded foundation of knowledge that can be put to immediate use to help people and organizations advance cognitive technology.

MIT recommends taking two core courses first. These are Machine Learning for Big Data and Text Processing: Foundations and Machine Learning for Big Data and Text Processing: Advanced. The Foundations course costs $2,500; the Advanced course costs $3,500. The remaining required 11 days are made up of elective classes, which last between two and five days each and cost between $2,500 and $4,700.

Prerequisites. The program is designed for technical professionals with at least three years of experience in computer science, statistics, physics or electrical engineering. MIT highly recommends this program for anyone in data analysis or for managers who need to learn more about predictive modeling.

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3. Artificial Intelligence: Business Strategies and Applications by University of California, Berkeley Executive Education and Emeritus

Key elements. Instead of teaching the how-tos of AI development, this certificate program is targeted at senior leaders looking to integrate AI into their organizations and managers leading AI teams. 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. In this course, you'll learn how to build an AI team, organize and manage successful AI application projects, and study the technology aspects of AI to communicate effectively with technical teams and colleagues.

Prerequisites. This program is mainly targeted toward 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 by Coursera

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

• Understand the basics of artificial intelligence; its applications and use cases; and key AI technologies, such as machine learning, deep learning 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.
• Apply computer vision techniques using Python, OpenCV and Watson.
• Develop custom image classification models and deploy them in the cloud.

Prerequisites. While the series is 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. Deep Learning Specialization by Andrew Ng via Coursera

Key elements. This is a comprehensive series of five intermediate to advanced courses covering neural networks and deep learning as well as their applications. Build and train deep neural networks, identify key architecture parameters, and implement vectorized neural networks and deep learning to applications. In this course, you will build a convolutional neural network and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.

You will also build and train recurrent neural networks, work with natural language processing (NLP) and word embeddings, and use HuggingFace tokenizers and transformer models to perform named entity recognition and question answering.

Prerequisites. Intermediate Python skills; basic programming; understanding of for loops, if/else statements, and data structures; and a basic grasp of linear algebra and machine learning.

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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 runs 18 hours and 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 learn to 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. This series is constructed for 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 2024: Build 7 AI + LLM & ChatGPT via Udemy

Key elements. Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT is a comprehensive online course designed to teach both the fundamentals as well as advanced concepts of AI, machine learning and deep learning.

The course is structured into 22 sections, comprising 130 lectures and 15.5 hours of video content. It also includes 19 articles, 12 downloadable materials, and numerous practical tasks and projects. The course costs $199, and all coding is done in Google Colab.

Key topics covered include state-of-the-art AI models, Q-learning and deep Q-learning, proximal policy optimization (PPO), large language models, transformers, NLP techniques for chatbots, Asynchronous Advantage Actor-Critic (A3C) and Soft Actor-Critic (SAC).

The seven projects are the following:

• Process Optimization AI using Q-Learning.
• Moon Lander AI with Deep Q-Learning.
• Pac-Man AI using Deep Convolutional Q-Learning.
• Kung Fu Fighter AI with A3C.
• Self-Driving Car AI using PPO.
• Another Self-Driving Car AI using SAC.
• AI Doctor Chatbot by fine-tuning Llama 2 LLM.

Upon completion, students receive access to three extra AI models: DDPG, or Deep Deterministic Policy Gradient; Full World Model; and Evolution Strategies & Genetic Algorithms. The course also includes a free 3-hour extra course on generative AI and LLMs with cloud computing.

Prerequisites. A basic understanding of high school math and some knowledge of Python programming are all that is required.

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8. Google Cloud's Introduction to Generative AI Learning Path

Key elements. Google Cloud's Introduction to Generative AI Learning Path covers what generative AI and large language models are for beginners. Since it's from Google, it is oriented around specific Google applications, which is only good if you are a Google shop. Tools used include Google Tools and Vertex AI. It includes a section on responsible AI, encouraging the learner to keep ethical practices around the generative AI in mind.

Prerequisites. None.

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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 consist of 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 note experience is not mandatory, but a 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 gain 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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Andy Patrizio is a technology journalist with almost 30 years' experience covering Silicon Valley who has worked for a variety of publications on staff or as a freelancer, including Network World, InfoWorld, Business Insider, Ars Technica and InformationWeek. He is currently based in southern California.