Certified Artificial Intelligence Professional (CAIP)

Certified Artificial Intelligence Professional (CAIP). Review available formats, prerequisites, current inclusions and certification terms before booking.

PECBExpert5 daysLiveSelf-pacedIn-house

Practitioner-led, taught by a working CISO

Taught by

Christophe Mazzola

Practicing CISO · Founder of Cyber Academy

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For

Right fit if you are.

✓AI professionals actively involved in developing and implementing AI technologies
✓Experienced AI practitioners who want to stay current with emerging trends and strengthen leadership skills
✓Data scientists focused on developing and optimizing AI models
✓IT managers overseeing AI projects and initiatives within their organizations
✓Risk and compliance officers managing AI-related risks and regulatory compliance
✓Executives such as CIOs, CEOs, and COOs who make strategic decisions involving AI
✓Professionals aiming to move into executive-level AI roles and needing a comprehensive understanding of the field

Honest call

NOT for. When to skip it.

We'd rather you keep your money than buy the wrong path.

✗Complete beginners with no programming exposure or AI background may find the pace of Day 2 onward challenging without prior self-study
✗Professionals seeking deep specialization in a single AI subdiscipline, such as advanced NLP research, will find the course breadth-focused rather than deeply specialized
✗Those looking for a non-technical management overview only may find the machine learning and deep learning sessions more detailed than needed

Outcomes

What you'll be able to do

1Explain foundational AI principles and describe how AI is applied across different domains
2Conduct data analysis and produce visualizations that support AI project decision-making
3Apply supervised, unsupervised, and reinforcement learning techniques to practical problems
4Implement neural network architectures including convolutional neural networks for deep learning tasks
5Describe how natural language processing systems and computer vision methodologies function
6Explain the role of robotics and expert systems in AI-driven automation
7Identify AI-related risks and apply compliance measures to mitigate them
8Develop ethical AI strategies that align with organizational values and applicable regulations

Programme

Day by day

Day 1Foundations of AI and Data Analysis

Foundational AI Principles and Applications
This module introduces the core concepts underpinning artificial intelligence, covering its history, key paradigms, and the range of domains in which AI is currently applied.
Data Analysis and Visualization Techniques
Participants learn how to perform fundamental data analysis and create meaningful visualizations that provide the insights needed to guide AI project development and evaluation.

By end of day

• Explain what AI is and map its principal application areas
• Produce data visualizations that surface actionable insights for AI projects

Day 2Machine Learning

Supervised and Unsupervised Learning
This module covers the theoretical basis and practical application of supervised and unsupervised machine learning algorithms, including model selection, training, and evaluation.
Reinforcement Learning
Participants explore how reinforcement learning enables AI agents to learn optimal behaviors through interaction with an environment, including key algorithms and use cases.

By end of day

• Build and evaluate machine learning models using supervised and unsupervised approaches
• Describe how reinforcement learning differs from other ML paradigms and where it is applicable

Day 3Deep Learning and Natural Language Processing

Neural Networks and Deep Learning Architectures
This module examines how neural networks are structured, how they are trained, and how advanced deep learning architectures such as convolutional neural networks extend their capabilities.
Natural Language Processing Systems
Participants gain an understanding of how NLP systems process, interpret, and generate human language, including core techniques and prominent real-world applications.

By end of day

• Implement a basic neural network and explain how a CNN processes input data
• Describe the key components of an NLP pipeline and their functions

Day 4Computer Vision, Robotics, AI Security, AI Strategy, Governance, and Risk Management

Computer Vision and Robotics
This module introduces computer vision methodologies for interpreting visual data and explains how robotics and expert systems are used to enable AI-driven automation.
AI Security and Risk Management
Participants learn to identify AI-specific security threats, apply risk mitigation measures, and ensure compliance with relevant regulations governing AI systems.
AI Ethics, Governance, and Strategy
This module covers how to develop ethical AI strategies, establish governance frameworks, and align AI initiatives with organizational values and societal expectations.

By end of day

• Identify security vulnerabilities in AI systems and describe appropriate countermeasures
• Draft an AI governance and ethics strategy aligned with organizational and regulatory requirements
• Connect computer vision and robotics concepts to practical automation scenarios

Day 5Consolidation and Exam Preparation

Cross-Domain Knowledge Review
This session revisits all seven competency domains examined during the week, helping participants consolidate understanding and identify areas requiring further review before assessment.
Exam Orientation and Q&A
Participants receive an overview of the PECB Certified Artificial Intelligence Professional exam structure and have the opportunity to address outstanding questions before sitting the assessment.

By end of day

• Identify personal knowledge gaps across all seven CAIP competency domains
• Approach the exam with clarity about its scope and structure

Public sessions

Upcoming public sessions

Open-enrolment cohorts. Pick a date and book your seat. Want a private cohort for your team instead? Request an in-house quote.

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Deep dive

Everything inside this certification

The detail behind the headline. Read at your own pace. Each section answers a buyer question we get on discovery calls.

Domain 1: Fundamental concepts and principles of artificial intelligence
Domain 2: Data analysis and Visualization
Domain 3: Building Machine Learning models
Domain 4-5: Concepts of Deep Learning and NLP
Domain 6-7-8: Knowledge and application of Computer Vision, Robotics and Expert Systems
Domain 9: AI Risk, Privacy and Compliance
Domain 10: AI Ethics, Governance , Strategy
Certification and examination fees are included in the price of the training course.
Participants will be provided with training course materials containing over 400 pages of information, practical examples, exercises, and quizzes.
An attestation of course completion worth 31 CPD (Continuing Professional Development) credits will be issued to the participants who have attended the training course.
Candidates who have completed the training course but failed the exam are eligible to retake the exam once for free within a 12 month period from the initial date of the exam.

Why Should You Attend?

Understand and navigate the latest AI trends and technologies.
Build and optimize AI systems that drive innovation.
Address critical challenges such as AI bias, privacy concerns, and compliance.
Strategically align AI solutions with organizational goals to maximize value.

Educational approach

Comprehensive Curriculum: The course combines theoretical knowledge with real-world examples to ensure participants gain both fundamental and advanced AI concepts.
Practical Exercises: Hands-on activities and projects simulate real-life scenarios, enabling participants to apply their skills effectively.
Interactive Learning: Group discussions and collaborative tasks for deeper engagement and shared learning experiences.
Certification Readiness: The course includes quizzes and exercises that closely align with the certification exam format.

FAQ

Buyers always ask

Does attending this training automatically award the CAIP certification?+

No. Completing the training course means you have participated in all instructional sessions, but it does not grant the PECB Certified Artificial Intelligence Professional credential. To obtain the certification, you must separately pass the PECB CAIP exam and satisfy PECB's credential requirements, which include relevant professional experience activities.

Cyber Academy delivers the training programme. The exam and certification are managed entirely by PECB, and candidates should consult PECB directly for current requirements and procedures.

How broad is the technical content, and is coding ability required?+

The course spans a wide range of AI topics, from data analysis and machine learning through to deep learning, NLP, computer vision, and AI governance. The curriculum is designed to give professionals both technical understanding and strategic perspective.

A general grasp of basic programming is recommended, but the course is not a software engineering programme. Participants who are not developers will still engage meaningfully with the governance, risk, ethics, and strategy content covered in the later days.

What competency domains does the PECB CAIP exam cover?+

According to PECB, the exam addresses seven domains: fundamental concepts and principles of AI; data analysis and visualization; building machine learning models; deep learning and natural language processing; computer vision and robotics; AI security; and AI ethics, governance, and strategy.

For details on the exam format, available languages, and sitting arrangements, candidates should refer to the official PECB List of Exams and Examination Rules and Policies.

Is this course focused more on technical skills or leadership and governance?+

The course deliberately covers both dimensions. Days 1 through 3 are more technically oriented, covering data analysis, machine learning, deep learning, and NLP. Day 4 shifts to applied topics including AI security, ethical strategy, and governance, making the programme relevant to both practitioners and leaders.

This breadth is intentional, as the CAIP credential is aimed at professionals who need to operate effectively across technical and strategic AI contexts.

What professional experience activities are recognized for the CAIP credential?+

According to PECB, recognized activities include fundamental data analysis, visualization, and preprocessing; implementing supervised and unsupervised machine learning models; hands-on work with deep learning concepts; and developing or enforcing AI governance, ethics, and risk management frameworks that promote transparency, fairness, and compliance.

Candidates should verify current credential requirements directly with PECB, as these may be updated over time.

Next step

Where this leads

Pillar guide

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EU AI Act readiness work for providers and deployers across the four risk tiers.

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