Top 10 AI certifications and courses for 2026

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

AI is on track to be the key technology that enables business transformation, giving companies a competitive edge. A 2024 International Data Corporation forecast shows that worldwide spending on AI -- including AI-enabled applications, infrastructure and related IT and business services -- will more than double to $632 billion in 2028, with a compound annual growth rate of 29% over the 2024-2028 forecast period. 

AI can help businesses be more productive by automating their processes, including using robots and autonomous vehicles, and by supporting their existing workforces with AI technologies such as assisted and augmented intelligence. Most organizations are working to implement AI in their business 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 

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, 60% demand at least a bachelor's degree. Another 18% require a PhD, while only 8% would consider a high school diploma. 

10 notable 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. 

Registration details 

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

Key elements. This certification program operates 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. 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. 

Registration details 

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

Key elements. Instead of teaching the how-to's 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. 

Registration details 

4. IBM AI Professional Certificate by Coursera  

Key elements. This beginner-level AI certification course is aimed at helping students gain job-ready skills in AI technologies, generative AI models and programming skills to build AI chatbots and apps. The six-month program consists of a 10 course series: 

• Introduction to Software Engineering 

• Introduction to Artificial Intelligence 

• Generative AI: Introduction and Applications 

• Generative AI: Prompt Engineering Basics 

• Introduction to HTML, CSS, and JavaScript 

• Python for Data Science, AI, and Development 

• Developing AI Applications with Python and Flask 

• Building Generative AI-Powered Applications with Python 

• Generative AI: Elevate Your Software Development Career 

• Software Development Career Guide and Interview Preparation 

The program runs for 6 months with 4 hours per week flexible self-paced learning. Students who successfully complete the program earn a professional certificate. 

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. 

Registration details 

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. 

Registration details 

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. 

Registration details 

7. Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL 

Key elements. Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL is a comprehensive online course designed to teach the tools and technologies of agentic AI, generative AI and reinforcement learning in order to create real-world AI applications. The course is structured into 22 sections, comprising 128 lectures and 15.5 hours of video content. Keep topics include Q-Learning; Deep Convolutional Q-Learning; Proximal Policy Optimization (PPO); LLMs; Low-Rank Adaptation (LoRA) and Quantization (QLoRa); Asynchronous Advantage Actor-Critic (A3C); Soft Actor-Critic (SAC); and Transformers.  

Students can practice these tools by building 8 different AIs: 

• Build an AI Agent with a Foundation Model (LLM) for business assistance, all powered by the Cloud. 

• Build an AI with a Q-Learning model and train it to optimize warehouse flows in a Process Optimization case study. 

• Build an AI with a Deep Q-Learning model and train it to land on the moon. 

• Build an AI with a Deep Convolutional Q-Learning model and train it to play the game of Pac-Man. 

• Build an AI with an A3C (Asynchronous Advantage Actor-Critic) model and train it to fight Kung Fu. 

• Build an AI with a PPO (Proximal Policy Optimization) model and train it for a self-driving car. 

• Build an AI with a SAC (Soft Actor-Critic) model and train it for a self-driving car. 

• Build an AI by fine-tuning a pre-trained LLM (Llama 2 by Meta) with Hugging Face and re-train it to chat with you about medical terms. 

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. 

Upon completion, students receive access to three extra AI models: Deep Deterministic Policy Gradient (DDPG), Full World Model and Evolution Strategies & Genetic Algorithms. Each of these extra AIs come with a video lecture explaining the implementation, a mini-PDF and the Python code.  

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

Registration details 

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. 

Registration details 

9. Artificial Intelligence Engineer (AIE) Certification 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. The ARTiBA certification has three tracks aimed at different educational and experience levels: 

AiE Track 1 – Bachelor’s Degree Pathway 

Requires a Bachelor’s degree in computer science, data science, artificial intelligence or related discipline (mathematics, statistics or computational sciences) from an accredited institution. Professional experience requirements include three years in hands-on programming or software engineering. 

AiE Track 2 – Master’s Degree Pathway 

Requires a Master’s degree in computer science, data science, artificial intelligence or related discipline (mathematics, statistics or computational sciences) from an accredited institution. Professional experience requirements include two years in software engineering, data engineering, machine learning or related technical roles. 

AiE Track 3 – Professional Experience Pathway 

There are no fixed academic requirements. The track is aimed at professionals with experience as self-taught engineers, AI startup builders or community-recognized technical work; also candidates without formal degrees who have published or deployed real-world AI or machine learning systems.   

Registration details 

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.