PECB Certified Artificial Intelligence Professional
What will you learn
01 Understand AI foundations Build broad understanding of AI concepts, data foundations and the AI solution lifecycle. | 02 Apply machine learning Understand supervised, unsupervised and model-evaluation approaches used in AI solutions. | |
03 Explore advanced AI Work across deep learning, generative AI, NLP, computer vision and intelligent systems. | 04 Address security and risk Identify AI-specific security issues and apply responsible AI, ethics and risk principles. | |
05 Connect AI to strategy Understand governance, compliance and how AI initiatives align with organisational objectives. | 06 Prepare for certification Consolidate the PECB competency domains through practical application and exam preparation. |
Who should attend?
- AI practitioners and aspiring AI professionals
- Data scientists and data analysts
- Machine-learning practitioners
- Developers working with AI-enabled solutions
- Technology and digital professionals
- AI project and product teams
- AI consultants
- Analytics professionals
- Technical managers responsible for AI initiatives
- Risk and governance professionals seeking stronger AI understanding
- Decision-makers involved in AI implementation
- Professionals seeking a broad PECB AI credential
Pre-requisites
- PECB recommends a general understanding of basic programming
- Basic familiarity with AI concepts beneficial
- Familiarity with data analysis useful
- Basic understanding of statistics beneficial
- Experience with Python or similar tools useful for practical labs
- No professional experience required for the Provisional credential
- Full professional credential requires two years' professional experience, including one year in AI
- Laptop required for practical exercises and virtual labs
Delivery format
- Hybrid – face-to-face + live stream
- Instructor-led PECB-aligned programme
- Practical hands-on labs
- Technical demonstrations and exercises
- Individual and group activities
- Knowledge checks and quizzes
Explore the twenty modules designed to build practical capability across AI foundations, data, machine learning, deep learning, generative AI, security, governance, strategy and PECB certification preparation.
Module 1: AI Foundations & the AI Landscape
What you'll explore:
- Defining artificial intelligence and intelligent systems
- Understanding major AI disciplines
- Exploring the evolution of AI technologies
- Identifying common enterprise AI applications
- Understanding the AI solution lifecycle
Module 2: Data Foundations for Artificial Intelligence
What you'll explore:
- Understanding the role of data in AI systems
- Identifying structured and unstructured data
- Assessing data quality
- Understanding training and testing data
- Recognising bias introduced through data
Module 3: Data Analysis & Visualisation
What you'll explore:
- Exploring data distributions
- Identifying trends and patterns
- Using descriptive analytical techniques
- Selecting meaningful visualisations
- Communicating data insights effectively
Module 4: Data Preparation & Preprocessing
What you'll explore:
- Cleaning and transforming datasets
- Managing missing values
- Handling outliers and inconsistencies
- Encoding and preparing variables
- Establishing suitable model-ready datasets
Module 5: Machine-Learning Foundations
What you'll explore:
- Understanding machine learning
- Differentiating supervised and unsupervised learning
- Understanding training, validation and testing
- Selecting suitable machine-learning approaches
- Connecting algorithms to business problems
Module 6: Supervised Machine Learning
What you'll explore:
- Understanding regression problems
- Understanding classification problems
- Exploring common supervised algorithms
- Training models using labelled data
- Evaluating supervised-model performance
Module 7: Unsupervised Machine Learning
What you'll explore:
- Understanding clustering
- Exploring dimensionality reduction
- Discovering patterns in unlabelled data
- Selecting suitable unsupervised methods
- Translating analytical results into business insights
Module 8: Model Evaluation & Optimisation
What you'll explore:
- Selecting appropriate performance metrics
- Understanding overfitting and underfitting
- Tuning model parameters
- Comparing alternative models
- Improving model reliability
Module 9: Deep-Learning Foundations
What you'll explore:
- Understanding artificial neural networks
- Exploring neurons, layers and activation functions
- Understanding training processes
- Exploring common deep-learning architectures
- Identifying suitable deep-learning applications
Module 10: Advanced Neural Networks
What you'll explore:
- Exploring convolutional neural networks
- Understanding recurrent and sequential architectures
- Exploring transformer-based approaches
- Understanding transfer learning
- Selecting architectures for different problem types
Module 11: Natural Language Processing
What you'll explore:
- Understanding NLP foundations
- Preparing text for analysis
- Understanding classification and sentiment analysis
- Exploring information extraction
- Identifying enterprise NLP applications
Module 12: Generative AI & Language Models
What you'll explore:
- Understanding generative-AI concepts
- Exploring large language models
- Understanding prompting and contextual interaction
- Identifying generative-AI use cases
- Understanding hallucination and reliability considerations
Module 13: Computer Vision
What you'll explore:
- Understanding image-based AI applications
- Exploring image classification
- Understanding object detection
- Identifying computer-vision business use cases
- Understanding data and performance considerations
Module 14: Robotics & Intelligent Systems
What you'll explore:
- Understanding AI-enabled robotics
- Exploring sensing and perception
- Understanding autonomous decision-making
- Identifying industrial and service applications
- Recognising safety and control considerations
Module 15: AI Security
What you'll explore:
- Understanding AI-specific security risks
- Identifying adversarial threats
- Protecting training and operational data
- Considering model and access security
- Embedding security into AI development
Module 16: AI Ethics, Responsible AI & Risk
What you'll explore:
- Understanding fairness and bias
- Addressing transparency and explainability
- Protecting privacy
- Establishing human accountability
- Applying responsible AI principles to real scenarios
Module 17: AI Governance & Compliance
What you'll explore:
- Establishing AI governance structures
- Defining roles and accountabilities
- Developing AI policies
- Understanding compliance obligations
- Monitoring responsible AI performance
Module 18: AI Strategy & Organisational Alignment
What you'll explore:
- Connecting AI initiatives with business strategy
- Identifying priority AI opportunities
- Assessing implementation feasibility
- Aligning AI investment with organisational objectives
- Measuring AI-enabled business value
Module 19: Integrated AI Application & Exam Preparation
What you'll explore:
- Connecting the seven PECB competency domains
- Working through integrated AI scenarios
- Reviewing practical laboratory lessons
- Identifying individual knowledge gaps
- Practising certification-style questions
Module 20: PECB CAIP Certification Examination
What you'll explore:
- Final examination briefing
- Review of PECB examination requirements
- Completion of the certification assessment
- Understanding the credential-application process
- Planning progression from Provisional to full professional certification
PRACTICAL TOOLS & RESOURCES
You will walk away with
A practical toolkit and learning resources to support hands-on AI application, certification preparation and continued professional development.
| ✓ | Official PECB learning materials Provided where the programme is delivered under the licensed PECB programme. |
01 Hands-on virtual AI labs | 02 AI terminology and concepts guide |
03 Data-analysis and preprocessing checklist | 04 Machine-learning algorithm comparison guide |
05 Deep-learning reference framework | 06 NLP use-case library |
07 Computer-vision and robotics use-case examples | 08 AI security checklist |
09 Responsible AI and ethics checklist | 10 AI governance framework |
11 Certification-domain study guide | 12 Exam-style quizzes and exercises |
13 Certification readiness assessment | 14 Individual revision plan |
15 31 CPD credits upon completion of the official PECB course | 16 One free exam retake for eligible participants within 12 months |
Practical resources you can keep using beyond the programme to support certification preparation and continued AI application.
CERTIFICATION BODY
PECB
CREDENTIAL
PECB Certified Artificial Intelligence Professional (CAIP)
- Seven examination competency domains
- Domains include AI fundamentals, data analysis and visualisation, machine learning, deep learning/NLP, computer vision/robotics, AI security, and AI ethics/governance/strategy
- Professional certification requires passing the PECB CAIP examination
- Provisional Artificial Intelligence Professional: no professional-experience requirement
- Certified Artificial Intelligence Professional: two years of professional experience, including one year in artificial intelligence
- Candidates must sign the PECB Code of Ethics
- Official programme provides 31 CPD credits
- Candidates who attend the official training and fail the exam are eligible for one free retake within 12 months
- Practical browser-based virtual labs are included in the official training
Schedule
Programme details to be announced soon; watch this space.
Connect with our team
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Dhisha Viswanathan
Programme Advisor - Digital Programme
