22 AI Certifications to Know

Demand for professionals with artificial intelligence skills has soared, with a McKinsey survey finding that over 70 percent of companies have adopted AI and a PwC survey confirming that jobs requiring AI specialist skills have grown over three times faster than all other jobs.

To help, many organizations are rolling out AI certification programs, courses and other training opportunities as a way for people to bolster their AI abilities.

 

Why Get an AI Certification? 

Up to 75 percent of knowledge workers across the world are already using generative AI tools, according to a Microsoft report. Responses in the same report further reveal that 71 percent of leaders prefer candidates with AI skills over more experienced candidates, and 77 percent would give early-career candidates with AI skills more responsibilities.

As a result, achieving an AI certification not only leads to greater job security for employees but also opens the door to career advancement opportunities. AI expertise provides a major advantage for job candidates as well, serving as a way to stand out from a packed talent pool and accelerate one’s career development.

Related ReadingArtificial Intelligence Careers: Tips From Experts

 

22 Top AI Certifications to Know

Whether you’re a seasoned professional looking to upskill, or a novice taking your first steps into the world of AI, these certifications can help increase your credibility in this highly competitive job market.

1. Professional Certificate in Computer Science for Artificial Intelligence - Harvard University

• Who it’s for: Any student or professional who wants to gain a broader understanding of computer science, programming and AI. 
• Topics and skills covered: Computer science, programming, AI algorithms, AI principles, machine learning and how to use AI in Python.
• Cost: $466.20, five months with 7-22 hours of learning per week. 
• Experience needed: No prior experience in computer science or AI is required. 
• Additional features: Certificate series includes two CS50 courses taught by Harvard computer science instructors.

2. Artificial Intelligence Engineer Certification - Artificial Intelligence Board of America

• Who it’s for: Professionals who want to build a career in AI engineering. 
• Topics and skills covered: Machine learning models, deep learning, natural language processing, human-computer interaction, cognitive computing and solving data and business challenges with AI. 
• Cost: $550, 180 days, self-paced.  
• Experience needed: Students must have at least an associate’s degree, as well as basic programming skills. 
• Additional features: Participants receive books and access to online materials before taking a certification exam within 180 days of registration.

3. Professional Certificate Program in Machine Learning & Artificial Intelligence - MIT Professional Education

• Who it’s for: Professionals who work in a technical field and want to learn how to apply AI and machine learning to data analysis.  
• Topics and skills covered: Machine learning, data analytics, deep learning and applying AI and machine learning to data analytics and mathematical concepts.
• Cost: Price depends on courses selected, 36 months, pace depends on courses and instructors.
• Experience needed: At least a bachelor’s degree and three years of experience working in a technical field is required. 
• Additional features: Participants can supplement two core courses with a range of electives that cover AI ethics, computer vision and other topics.

4. AI Engineering Professional Certificate - IBM

• Who it’s for: Professionals who want to build a career as an AI or ML engineer.  
• Topics and skills covered: Fundamentals of machine learning and deep learning, programming languages like Python, computer vision, NLP, object recognition and applying ML to big data. 
• Cost: Free with Coursera account, two months with 10 hours of learning per week, self-paced. 
• Experience needed: Intermediate coding experience and a background working as an AI or ML engineer. 
• Additional features: Participants who complete the program earn a LinkedIn certificate and gain access to resume, interview and career support.

5. NVIDIA-Certified Associate Generative AI LLMs - NVIDIA

• Who it’s for: Professionals in technical roles like data scientist, ML engineer and software engineer who are looking to gain foundational knowledge of generative AI, large language models (LLMs) and how to apply basic concepts to AI applications. 
• Topics and skills covered: Machine learning, neural networks, prompt engineering, data analysis, software development, Python libraries and deploying LLMs.
• Cost: $135, one-hour time limit.
• Experience needed: Basic knowledge of generative AI and LLMs is required. 
• Additional features: Participants who pass the exam receive a certification that lasts for two years before the exam must be retaken.

6. Fundamentals of Google AI for Web-Based Machine Learning - Google

• Who it’s for: Professionals looking to learn the fundamentals of AI, ML and deep learning — and how they relate to each other and data.
• Topics and skills covered: AI basics, machine learning, deep learning, programming languages like JavaScript (JS), ML libraries and building web applications.
• Cost: $538.20, three months with several hours of learning per week, self-paced. 
• Experience needed: No prior experience is required.
• Additional features: The program consists of two courses — a Google AI course for beginners and a Google AI course for JS developers.

More on Artificial IntelligenceExplore Built In’s AI Coverage

7. Generative AI with Large Language Models - AWS and DeepLearning.ai

• Who it’s for: Professionals with a foundational understanding of AI topics looking to integrate generative AI and LLMs into their work. 
• Topics and skills covered: Generative AI principles, building LLMs, training models and applying generative AI to business scenarios. 
• Cost: Free with Coursera account, three weeks with five hours of learning per week, self-paced. 
• Experience needed: Prior experience in Python, machine learning basics, working with training data and taking the ML or deep learning specialization from DeepLearning.AI is required. 
• Additional features: The course consists of three separate modules and culminates with participants receiving a LinkedIn certificate upon completion. 

8. Artificial Intelligence A-Z 2024: Build 7 AI LLM and ChatGPT - SuperDataScience Team

• Who it’s for: Students and professionals looking to develop their knowledge of AI, machine learning and deep learning.
• Topics and skills covered: AI, machine learning, deep learning, reinforcement learning, building AI applications, LLMs, deep q-learning and convolutional neural networks.
• Cost: $199.99, 15.5 hours, self-paced.
• Experience needed: Basic knowledge of Python and completion of high-school-level math is required.
• Additional features: Participants who complete the course get access to three additional AI models that can be used for a humanoid and a self-driving car.

9. Artificial Intelligence Graduate Certificate - Stanford University

• Who it’s for: Professionals interested in developing more in-depth AI expertise and how to apply AI in their workplaces. 
• Topics and skills covered: AI principles, machine learning, deep learning, probability models, computer vision, robotics, NLP, data mining and AI-driven decision-making.
• Cost: $19,682-$24,224, up to two years with 15-20 hours per week of learning.
• Experience needed: A bachelor’s degree with a GPA of at least 3.0, college-level calculus and algebra, programming experience and knowledge of probability theory and probability distribution concepts are required.  
• Additional features: Participants must complete two core courses and three electives, receiving a certificate for every course where they earn a B grade or higher. 

10. Microsoft Certified: Azure AI Engineer Associate - Microsoft

• Who it’s for: Professionals who work as AI engineers and want to demonstrate their expertise in the Microsoft Azure AI platform. 
• Topics and skills covered: Azure AI basics, programming languages like C#, responsible AI principles, NLP solutions, computer vision solutions and building AI applications. 
• Cost: $165, 100-minute time limit. 
• Experience needed: Experience working as an AI engineer and working with Microsoft Azure AI is required. 
• Additional features: Participants gain access to exam preparation resources, including a practice assessment and training videos.

Searching for a Job?These Companies Are Hiring AI Engineers

11. AI for Everyone - DeepLearning.ai

• Who it’s for: Anyone interested in developing a foundational knowledge of AI. 
• Topics and skills covered: AI common terms, AI’s capabilities, AI business applications, AI strategy and AI ethics. 
• Cost: Free with a Coursera account, three weeks with two hours of learning per week, self-paced.
• Experience needed: No prior experience required.  
• Additional features: The course is made up of four modules, and participants who complete the course earn a LinkedIn certificate.

12. Artificial Intelligence: Business Strategies and Applications - University of California - Berkeley

• Who it’s for: Professionals looking to learn the basics of AI and how to leverage AI technologies in business settings.
• Topics and skills covered: AI’s capabilities, generative AI applications, running AI applications, machine learning basics, deep learning, robotics and common AI pitfalls. 
• Cost: $2,714, two months with four to six hours of learning per week.
• Experience needed: No prior technical experience is required.
• Additional features: Participants are taught by instructors through live online sessions and complete a capstone project at the end of the course.

13. Designing and Building AI Products and Services - MIT xPro

• Who it’s for: UX and UI designers, AI startup founders, technology consultants, technical product managers and those in other technology professions. 
• Topics and skills covered: AI product design, building AI models, machine learning algorithms, human-machine interfaces and problem-solving with ML techniques. 
• Cost: $2,832, eight weeks with six hours of learning per week. 
• Experience needed: Experience in a technical profession and basic knowledge of calculus, linear algebra, statistics, probabilities and Python is required. 
• Additional features: Participants get to present an AI project proposal to internal stakeholders and investors, and they earn five continuing education units upon completing the course.

More on AI and WorkAI Taking Over Jobs: What We Know About the Future of Jobs

14. AI Developer Professional Certificate - IBM

• Who it’s for: Professionals looking to acquire the skills needed to launch a career in an AI profession.
• Topics and skills covered: Software engineering AI fundamentals, generative AI, prompt engineering, programming languages like HTML and building AI applications.
• Cost: Free with Coursera account, six months with four hours of learning per week, self-paced.
• Experience needed: No AI or programming experience is required.
• Additional features: Participants who complete the course are awarded with an IBM digital badge and display employable skills to launch an AI career in six months.

15. Jetson AI Certification - NVIDIA

• Who it’s for: The AI Specialist course is open to anyone looking to broaden their AI knowledge while the AI Ambassador course is tailored to educators who want to enhance their AI expertise.
• Topics and skills covered: NVIDIA Jetson basics, Jupyter Notebook, training deep neural networks and machine learning.
• Cost: Free, self-paced.
• Experience needed: Basic knowledge of Python and Linux for both courses while teaching experience is required for the Ambassador course.
• Additional features: Participants must complete a hands-on assessment where they submit an open-source project that involves problem-solving using NVIDIA Jetson.

16. Professional Machine Learning Engineer Certification - Google Cloud

• Who it’s for: Professionals who work as ML engineers or other relevant technical roles who want to demonstrate their expertise in ML engineering. 
• Topics and skills covered: Building ML models, developing AI apps, training models, data processing, running ML experiments, automating model training, tracking metadata and testing ML solutions. 
• Cost: $200, two-hour time limit. 
• Experience needed: At least three years of relevant work experience with one year of developing and managing Google Cloud solutions is required. 
• Additional features: Participants can review an exam guide, practice with sample questions and join webinars to prepare for the certification exam.

17. Post Graduate Program in AI and Machine Learning - Purdue University

• Who it’s for: Professionals looking to advance their careers in AI- and ML-related fields. 
• Topics and skills covered: AI, ML, deep learning, Python, data science, generative AI basics, prompt engineering, ChatGPT, NLP, speech recognition, computer vision and reinforcement learning. 
• Cost: $4,300, 11 months, pace depends on instructors.
• Experience needed: A bachelor’s degree with at least 50-percent marks, basic knowledge of programming and mathematics concepts and at least two years of relevant work experience are required. 
• Additional features: Participants can engage with over 25 hands-on projects and practice using over 20 AI tools.

18. The Graduate Certificate in Ethical Artificial Intelligence - San Francisco State University

• Who it’s for: Graduate students and professionals interested in strengthening their understanding of AI ethics and the social and legal implications of using AI. 
• Topics and skills covered: AI ethics, data mining, pattern analysis, AI compliance, AI applications and philosophical issues with AI. 
• Cost: Depends on registration status with SFSU, 10 course units, pace depends on instructors. 
• Experience needed: SFSU students must be graduate students and follow university procedures while non-SFSU students must have at least a bachelor’s degree with a 3.0 GPA and apply through Cal State Apply. 
• Additional features: Participants must complete a series of courses, culminating in a research and reflection paper that includes three courses of independent study.

19. CertNexus Certified Artificial Intelligence Practitioner Professional Certification - CertNexus

• Who it’s for: Data science professionals transitioning to the AI field who want to stand out from other job candidates and professionals. 
• Topics and skills covered: Business problem-solving with AI and ML, ML workflows, designing ML models and building decision trees and neural networks. 
• Cost: Free with Coursera account, two months with 10 hours of learning per week, self-paced. 
• Experience needed: Basic knowledge of AI concepts, experience with databases and experience with advanced programming languages are required.  
• Additional features: Participants who complete the course earn a LinkedIn certificate and can achieve industry certification after passing the exam.

20. Applied Generative AI for Digital Transformation - MIT Professional Education

• Who it’s for: Professionals of various backgrounds, especially senior leaders, technology leaders, senior managers, mid-career executives, innovation managers, sales and product managers, marketing professionals, customer experience professionals and venture capital investors.
• Topics and skills covered: Generative AI, automation strategies, digital transformation, reinforcement learning, prompt engineering, AI ethics and the risks of AI.
• Cost: $3,125, three weeks with up to 14 hours of learning per week.
• Experience needed: No prior experience is required.
• Additional features: Participants who finish the course know how to use generative AI tools and automate workflows to improve business processes.

21. Certified Artificial Intelligence Scientist - United States Artificial Intelligence Institute 

• Who it’s for: Senior-level professionals and leaders in the AI and business fields who want to sharpen their AI expertise and learn how to strategically apply AI to business contexts.
• Topics and skills covered: AI essentials, machine learning techniques, deep learning fundamentals, computer vision, generative AI, product management, explainable AI, AI ethics, engineering management and deploying AI and ML in the cloud.
• Cost: $894, 4-25 weeks with 8-10 hours of learning per week, self-paced.
• Experience needed: A bachelor’s degree in the STEM field and at least five years of work experience in AI or a related field are required for the initial path, with stricter requirements for more advanced paths.
• Additional features: The USAII provides an online resource center that includes study books and self-paced videos to help participants reinforce and practice new concepts.

22. ChatGPT / AI Ethics: Ethical Intelligence for 2024 

• Who it’s for: Leaders and managers who want to ensure they apply AI ethically within their organizations. 
• Topics and skills covered: AI ethical principles, practicing confidentiality in the digital age, fairness, bias, intellectual property and the ethical power of caring.
• Cost: $74.99, three hours, self-paced. 
• Experience needed: No prior experience is required. 
• Additional features: Participants close out the course by considering real-world examples of AI ethics including AI in college essays, voice cloning and AI used in filmmaking.

 

How to Choose an AI Certification

An AI certification course or program is only effective if it meets the needs of its participants. When it comes to selecting the right option for you, here are a few factors to consider.  

1. Establish Your Career Goals

Define your career goals before looking at courses. For example, are you interested in an AI certification program because you want to learn new skills for your current role, become qualified for a higher-ranking role or make a career change? Answering this question can help you narrow down offerings to the ones most relevant to your career ambitions.

2. Determine Personal Capacity   

Keep your personal boundaries in mind when exploring AI certifications. Consider variables like your financial limits, availability and learning preferences. Making sure a course aligns with your personal circumstances will ensure you have the best learning experience possible.

3. Study the Course Content

Review the curriculum to see if it addresses topics you’re interested in. Does the course content include skills, technologies and techniques relevant to your career goals or industry? Check whether a program supplements lessons with hands-on projects, which can translate into real-world experience that employers are looking for.

4. Assess the Return on Investment

Reflect on the benefits of the course and how it helps you get to where you want to go. Does it come with a certification recognized by industry leaders? Does it offer networking opportunities with top companies and professionals? Think beyond the course content and weigh any lasting advantages to decide whether it’s a worthy investment.

Frequently Asked Questions