The age of AI jobs is here! There’s a significant uptick in the hiring of AI-related skills in IT and tech companies. Companies today are hiring more AI/ML engineers than ever.
Updated October 2026. We re-checked the level, duration and cost of each program on the providers’ own pages, corrected the salary attribution (the linked figure is from Indeed, not Glassdoor) and added links to each provider.
As companies are increasingly focusing on digital transformation, they are looking for people who are equipped with AI-related skills. Roles like AI trainer, NLP Engineer, AI Business Strategist, Chief AI Officer (CAIO), etc. are on the rise. Not to mention the handsome salaries that companies are paying to attract top talent for these roles. When this article was first published, Indeed listed an average base salary of $156,691 for an AI/ML Engineer in the U.S.; as of October 2026, Indeed lists $153,066.
What’s more, AI is a highly dynamic field, and keeping up with new technologies is essential to thrive. Whether you’re a fresher or a seasoned AI pro, continuous learning is important to move forward in your career – and the AI career isn’t different in this respect.
In this article, we will cover AI certifications that can help you build a strong skill set and enable you to thrive in your AI career.
Best AI certifications to move forward in your career
There are ample AI certifications available online and offline. But which is the best one for you? Finding the one that matches your skill level, aspirations, and budget can be time-consuming. We’ve listed some of the most popular AI certifications below to consider and get started with your next learning goal. Prices and schedules change often, so confirm the details on each provider’s page (linked in the Sources section) before you enroll.
Whether you’re a fresher looking to learn from scratch and enter the industry, advance your skills to a new level, or transition to an entirely new AI role, the below certifications can help.
1. IBM AI Developer Professional Certificate by Coursera
Level: Beginner (both people with technical and non-technical backgrounds can take the course)
Duration: 10-course series (6 months, 4 hours a week)
Cost: Free to enroll; the certificate is included with a paid Coursera subscription such as Coursera Plus, and financial aid is available
Course overview:
The certification covers fundamentals of applied AI including machine learning, deep learning, and neural networks. Along with the fundamentals, the course gives you hands-on experience in building AI applications such as virtual assistants, chatbots, etc., using IBM Watson AI services and APIs. The course also covers Python. You will be equipped with advanced skills including computer vision and machine learning by working on real projects.
Skills covered:
- Python (for both data science and AI)
- Using IBM Watson and its APIs to build AI applications
- Building Chatbots
- Introduction to computer vision and its applications
2. UPenn’s Artificial Intelligence and Machine Learning Essentials with Python Specialization by Coursera
Level: Intermediate (Experience with Python programming is required)
Duration: 4-course series (4 months, 8 hours a week)
Cost: Free to enroll; the certificate is included with a paid Coursera subscription such as Coursera Plus
Course overview:
The intermediate course from UPenn is a comprehensive course to kickstart your learning in AI and machine learning. From the history of artificial intelligence and training a deep learning model, the course builds a strong foundation for learners to kick-start their career in AI.
Skills covered:
- Python
- Probability and statistics
- Search algorithms – A *search, depth-first search, breadth-first search, and more
- Deep learning models – perceptron, neural networks, backpropagation
3. Artificial Intelligence Graduate Certificate by Stanford University of Engineering
Level: Intermediate (programming skills are required)
Duration: Graduate-level courses taken one at a time (instructor-led)
Cost: Tuition varies by course; check Stanford Online for current fees
Course overview:
The certificate is earned by completing a set of required and elective graduate-level courses from Stanford’s artificial intelligence curriculum. Course options, requirements and fees are updated regularly, so check the program page on Stanford Online before you apply.
Skills covered:
- Under required courses, you will learn:
- Supervised and unsupervised learning
- Learning theory
- Reinforcement learning and control
Under elective courses, you have a multitude of choices, including robotics, deep learning, etc.
4. MIT’s Professional Certificate Program in Machine Learning & Artificial Intelligence
Level: Advanced (designed for professionals with at least three years of professional experience and a bachelor’s degree in a technical area)
Duration: At least 16 days of qualifying courses, completed within 36 months
Cost: Non-refundable application fee of $325, plus separate fees for each course (for example, $2,500 for the two-day Machine Learning for Big Data and Text Processing: Foundations course, as of October 2026)
Course overview:
This is an in-person program at MIT, with courses mostly held in June, July and August on MIT’s campus, and it equips you with advanced skills in machine learning and AI. The program is relevant both for learners who want to sharpen their fundamentals and for advanced learners: you must take at least one of the Machine Learning for Big Data and Text Processing courses (beginners start with Foundations, while those with prior experience may start with Advanced) and complete a total of at least 16 days of qualifying courses to earn the certificate.
Skills covered:
- Big Data
- Text processing
- Predictive modeling
5. UC Berkeley’s Artificial Intelligence: Business Strategies and Applications
Level: Advanced
Duration: 2 months, online (4-6 hours per week)
Cost: $3,050 (as listed by UC Berkeley Executive Education in October 2026)
Course overview:
The course focuses on building and developing insights for AI strategy from a business perspective. The course delves into the applications of AI in various aspects of the business – including supply chain, automation, personalizing customer experience, and many more. By the end of the course, you would have built a project focusing on implementing an org-wide AI project. The course covers – natural language processing, robotics, computer vision, neural networks, etc.
Skills covered:
- Artificial intelligence and its applications
- Organize and manage successful artificial intelligence application projects
- Leverage generative AI models and simulations for prediction
6. Deep Learning Specialization by Andrew Ng
Level: Intermediate (Requires experience in Python programming, understanding of for loop, data structures, and basic understanding of linear algebra and ML)
Duration: 5-course series (3 months, 10 hours a week)
Cost: Free to enroll; the certificate is included with a paid Coursera subscription such as Coursera Plus
Course overview:
The 5-course foundational course prepares you to develop AI technology. You will gain an understanding of deep learning by building and training neural networks using tools like TensorFlow and Hugging Face. By the end of the course, you would build a Convolutional Neural Network.
Skills covered:
- Convolutional Neural Networks
- LSTMs
- Recurrent Neural Networks
- Transformers
- Strategies to improve neural network architectures such as Dropout, BatchNorm, Xavier m /He initialization
- TensorFlow
7. Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning
Level: Intermediate (Requires experience in Python coding)
Duration: 4 modules (about 2 weeks at 10 hours a week)
Cost: Free to enroll on Coursera; the certificate is included with a paid subscription. The course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate.
Course overview:
The course focuses on enabling learners to use TensorFlow inside and out. You will learn to build, train, and optimize a neural network using TensorFlow.
Skills covered:
- TensorFlow
- Neural networks
- Building, training, and improving the neural network
8. Artificial Intelligence Engineer by ARTiBA
Level: Intermediate (Requires programming skills)
Duration: Up to 180 days from registration to take the exam (extendable by another 180 days for a USD 100 fee)
Cost: USD 750, all inclusive (ARTiBA fee effective November 2025)
Course overview:
The comprehensive certification validates your knowledge and skills required to kick-start a career in AI development. The course will help you develop expertise in AI and ML methodologies. You will learn the essentials of AI & ML, Natural Language Processing, Neural Networks, and Deep Learning. By the time you complete the course, you will have the confidence to think, build, train, and run machine learning models.
Skills covered:
- Supervised learning
- Unsupervised learning
- Ensemble learning
- Heuristic search techniques
- Building games with AI
- Probabilistic reasoning for sequential data
- Objection detection and tracking
9. Google Cloud’s Introduction to Generative AI
Level: Introductory
Duration: 45 minutes (microlearning course that awards a badge)
Cost: Check Google Skills (formerly Google Cloud Skills Boost) for current access options
Course overview:
The introductory course from Google explains what Generative AI is and how it differs from traditional machine learning models. You will learn to develop your own Gen AI app using Google tools.
Skills covered:
- What is Generative AI?
- Generative AI models
- Building Generative AI applications
10. Certified Artificial Intelligence Scientist (CAISTM) by the United States Institute of Artificial Intelligence
Level: Advanced
Duration: 4-25 weeks, self-paced (8-10 hours per week)
Cost: US $1,195, all inclusive (as listed by USAII in October 2026; installment payments available)
Course overview:
The certification validates a Senior AI professional and business leader’s ability to craft AI strategies and solutions that transform the business. Along with mastering the essentials of AI, you will learn strategic growth and product management. The course also includes lessons on Generative AI, Engineering Management, and computer vision.
Skills covered:
- Essentials of AI and machine learning
- Deep learning and Generative AI
- Strategic growth and product management
- Generative AI
- Computer vision
- Deep learning
Conclusion
Artificial intelligence is changing the face of every industry with its applications. Professional AI certifications can equip you with the skills required to adapt and thrive in the changing landscape. Whether you need to upskill or embark on an entirely new career path in artificial intelligence, certifications can help. So, go register for your certification today.
Related: How To Become a Business Intelligence Analyst: 5 Key Skills to Master
Related: What Is Lifecycle Marketing And Why Is It Important For A Business?
Sources
- Coursera: IBM AI Developer Professional Certificate
- Coursera: AI and Machine Learning Essentials with Python (University of Pennsylvania)
- Stanford Online: Artificial Intelligence Graduate Certificate
- MIT Professional Education: Professional Certificate Program in Machine Learning & Artificial Intelligence
- UC Berkeley Executive Education: Artificial Intelligence: Business Strategies and Applications
- Coursera: Deep Learning Specialization
- Coursera: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- ARTiBA: Artificial Intelligence Engineer (AiE) Certification
- Google Skills: Introduction to Generative AI
- USAII: Certified Artificial Intelligence Scientist (CAIS)
