Professional Training

Prerequisites

There are no formal prerequisites. Participants should have general digital literacy, basic Python programming skills, familiarity with variables, conditions, loops, functions and collections and introductory data-analysis knowledge. Knowledge equivalent to PCEP Python Certified Entry Level Programmer and PCED Python for Data Analytics level is desirable.

Who should attend

• Individuals beginning a career in Artificial Intelligence
• Aspiring AI specialists and junior data professionals
• Students and career changers
• Developers seeking an introduction to applied AI concepts
• Business professionals who work with AI-enabled systems
• Automation, operations and technical-support personnel
• Educators introducing AI literacy and computational thinking
• Candidates intending to progress to the PCEI-30-01 examination

Learning Objectives

At the conclusion of this course, attendees will be able to:

• Explain the fundamental concepts and terminology of Artificial Intelligence
• Distinguish narrow AI from the concept of general AI
• Identify the principal components and subfields of AI
• Explain how AI systems learn from data
• Distinguish training, testing and inference
• Compare supervised, unsupervised and reinforcement learning
• Recognise common machine-learning algorithms and applications
• Implement simple rule-, distance- and grouping-based logic in Python
• Interpret accuracy, precision, recall and confusion matrices
• Load, clean, organise and analyse small datasets
• Prepare features and normalise numerical values
• Create and interpret basic data visualisations
• Explain the foundations of neural networks and deep learning
• Describe introductory NLP and computer-vision concepts
• Explain how generative AI and large language models operate
• Construct and refine effective prompts
• Recognise hallucinations, bias, privacy risks and unsafe AI interactions
• Apply responsible, transparent and human-centred AI principles
• Identify feasible opportunities for small AI projects
• Communicate AI results and limitations to different audiences

Day 1 - Artificial Intelligence and Machine Learning Fundamentals

Core AI terminology, applications, learning concepts, capabilities, limitations and the principal types of machine learning.

Understanding Artificial Intelligence

Defining Artificial Intelligence
Agents, environments and inference
Input, processing and output
Data, algorithms and conceptual models
Narrow AI and General AI
Common AI capabilities
Classification, prediction and pattern recognition
Everyday AI applications
Search, navigation and recommendation systems
Assistants, image processing and text processing

AI Subfields

Machine learning
Deep learning
Natural language processing
Computer vision
Robotics
Generative AI
Large language models
Matching business problems to AI subfields

How AI Systems Learn

Learning from examples
Features and labels
Labelled and unlabelled data
Training and inference
Predictions and feedback loops
Generalisation and robustness
The effect of noise, bias and missing data

Capabilities and Limitations

Tasks AI performs effectively
Lack of context and common failure cases
Misclassification
Hallucinations
Dependence on data quality and diversity
Appropriate and inappropriate uses of AI
Situations requiring human judgement

Planning an AI Solution

Defining the problem
Identifying objectives
Data availability and suitability
Stakeholder requirements
Project constraints
Success criteria and evaluation measures
Deciding whether AI is appropriate

Types of Machine Learning

Supervised learning
Classification and prediction
Unsupervised learning
Clustering and pattern discovery
Reinforcement learning
Agents, actions, environments and rewards
Selecting a learning type for a problem

Practical work: analyse real-world problems, determine whether AI is appropriate and identify the most suitable AI subfield and learning approach.

Day 2 - Machine Learning Logic and Evaluation with Python

Machine-learning workflows, simple Python implementations and the evaluation of classification results.

The Machine Learning Workflow

Data collection
Data cleaning and preparation
Feature selection
Training
Evaluation
Testing
Inference
Training and testing datasets
Reasons for separating training and testing data
Recognising missing values, outliers and inconsistent types

Common Machine Learning Algorithms

Linear models
Decision trees
k-nearest neighbours
k-means clustering
Naive Bayes
Rule-based systems
Classification and clustering
Matching algorithms to problems
How models use rules, distances and patterns

Simple AI Logic in Python

Representing examples with lists and dictionaries
Implementing rule-based classifications
Combining conditions into decision rules
Creating a basic sentiment classifier
Grouping values using Python
Calculating Euclidean distance
Finding the nearest data point
Implementing introductory k-NN-style logic
Writing simple AI-style programs without an external framework

Evaluating Classification Results

Correct and incorrect predictions
Accuracy
True positives and true negatives
False positives and false negatives
Confusion matrices
Precision
Recall
Choosing suitable evaluation measures
Recognising good and poor model performance

Model Behaviour

Overfitting
Underfitting
Generalisation
Training performance compared with testing performance
Model limitations
When additional or better data is required

Practical work: develop and evaluate a small Python classifier using rules and distance calculations. Produce a confusion matrix and calculate accuracy, precision and recall.

Day 3 - Data Handling, Analysis and Visualisation

Loading, cleaning, analysing, preparing and visualising data for introductory AI workflows.

Loading and Organising Data

Reading text, CSV and JSON data
Lists and dictionaries
Lists of dictionaries
Nested data structures
Accessing and modifying nested values
Converting between strings and numbers
Preparing consistent records

Cleaning Data

Identifying missing values
Correcting type mismatches
Resolving formatting inconsistencies
Detecting duplicate records
Detecting incorrect or inconsistent labels
Recognising noise and outliers
Assessing dataset suitability
The relationship between data quality and model reliability

Analysing Data with Python

Mean and median
Minimum and maximum values
Frequency counts
Sorting by attributes
Grouping and summarising data
Identifying common categories
Detecting patterns and trends
Using the math module

Vectors, Distances and Similarity

Representing features as vectors
Euclidean distance
Manhattan distance
Basic similarity measures
Normalising values to a common range
Scaling numerical features
Understanding when normalisation is required
Preparing feature sets for simple AI tasks

Preparing Data for Machine Learning

Introductory pandas operations
Loading tabular data
Selecting columns
Filtering rows
Combining and restructuring data
Flattening nested structures
Feature selection
Feature extraction
Preparing data before model training

Visualising Data

Matplotlib fundamentals
Line charts
Bar charts
Histograms
Titles, labels and legends
Selecting an appropriate chart
Interpreting trends and distributions
Identifying possible outliers
Using visualisation to assess data quality and readiness

Practical work: load, clean and explore a dataset; prepare numerical features; calculate distances; and create visualisations that assess whether the data is suitable for an AI task.

Day 4 - Neural Networks, Deep Learning and Generative AI

Neural-network foundations, NLP, computer vision, generative AI, prompt engineering and pre-trained models.

Neural Network Fundamentals

Artificial neurons
Inputs and outputs
Weights and biases
Layers
Activation functions
Feedforward processing
Errors and loss
Backpropagation at a conceptual level
Adjusting weights to reduce errors
Shallow and deep networks

Machine Learning and Deep Learning

Classical machine learning
Deep learning
Differences between traditional algorithms and neural networks
Data and computational requirements
Advantages and limitations of deep models
Image and speech applications
Selecting classical or deep-learning approaches

Natural Language Processing

Representing text for computers
Tokens and sequences
Introductory embeddings
Sentiment analysis
Translation
Summarisation
Text classification
Rule-based and machine-learned NLP
The importance of context

Computer Vision

Images as numerical arrays
Pixels and colour channels
Image classification
Object detection
Image segmentation
Convolutional neural networks at a conceptual level
Face recognition and document scanning
Autonomous-system applications

Generative AI and Large Language Models

Generative and predictive AI
Text, image, audio and code generation
Large language models
Next-token prediction
Context and context limitations
Model strengths and weaknesses
Creativity, inconsistency, bias and hallucinations
Generative AI within the wider AI ecosystem

Prompt Engineering

Context-and-task prompt structure
Providing clear instructions
Specifying audiences, formats and constraints
Refining prompts iteratively
Prompts for summarisation and explanation
Prompts for classification and transformation
Evaluating prompt results
Prompt injection and unsafe requests
Recognising when human verification is required

Pre-Trained Models and Deployment

Training and inference
Using pre-trained models
Transfer learning
Model reuse
Deployment concepts
OCR, recommendation systems and chatbots
Applying trained models without training from scratch

Practical work: compare prompts for a set of AI tasks, evaluate outputs for accuracy and safety, and document improvements made through iterative refinement.

Day 5 - Responsible AI and the Integrated AI Project

Ethics, safety, critical evaluation, project planning, collaboration, communication and an integrated AI project.

Ethical Risks and AI Safety

Bias and discrimination
Unfair outcomes
Harmful stereotypes
Privacy and data-protection concerns
Hallucinations and misinformation
Inappropriate or harmful outputs
Safe and unsafe AI uses
Identifying potentially high-risk applications

Safe and Secure AI Use

Personal and sensitive information
Passwords and confidential documents
Data minimisation
Responsible input handling
Adhering to organisational policies and terms of use
Attempts to manipulate or bypass system controls
Recognising unexpected or harmful behaviour
Knowing when to stop or escalate

Social and Economic Impact

AI in business, education and public services
Productivity and innovation
Changes to occupations and workflows
Reskilling requirements
Job displacement
Inequality and the digital divide
Evaluating positive and negative effects

Responsible and Human-Centred AI

Fairness
Transparency
Accountability
Explainability
Human-in-the-loop processes
Human oversight
Verifying outputs and sources
Applying human judgement
Trustworthy AI deployment

Critical Evaluation of AI Outputs

Detecting incorrect and misleading statements
Hallucinations and unsupported claims
Logical errors and contradictions
Cross-checking information
Evaluating consistency and reliability
Recognising bias
Deciding when an automated output should be rejected

Planning an AI Project

Identifying suitable AI opportunities
Project goals, inputs and outputs
Data and domain-knowledge requirements
Constraints and feasibility
Data volume and model complexity
Compute, storage and time requirements
AI service and API costs
Usage limits
Comparing AI with simpler rule-based solutions
Cost-benefit considerations

Collaboration and Communication

Analyst, developer, domain-expert and reviewer roles
Shared responsibilities
Version-control concepts
Pair programming and code review
Peer feedback
Documenting decisions
Reproducibility
Explaining methods, outputs, errors and limitations
Communicating with technical and non-technical audiences

Integrated AI Project

Selecting an appropriate AI use case
Defining objectives and success measures
Loading and cleaning a small dataset
Selecting and preparing features
Implementing rule- or distance-based AI logic in Python
Evaluating results with basic metrics
Visualising relevant findings
Identifying ethical risks and limitations
Considering costs and resource requirements
Producing a concise project report
Presenting recommendations to a non-technical audience

Optional Day 6 - PCEI Examination Preparation Workshop

A separate one-day instructor-led workshop for candidates who have completed the five-day course or possess equivalent knowledge.

PCEI Examination Overview

Examination structure and current syllabus
The six examination blocks and their weightings
Single-select and multiple-select questions
Scenario-based questions
Interactive items
Managing the available examination time
Understanding the 75% passing requirement

Structured Syllabus Review

Artificial Intelligence fundamentals
Machine-learning fundamentals
Data handling, analysis and visualisation
Neural networks, deep learning and generative AI
Responsible AI, ethics and critical thinking
AI projects, collaboration and communication

Question-Answering Techniques

Reading questions precisely
Identifying the concept being tested
Evaluating Python logic without executing it
Calculating distances and basic model metrics
Interpreting confusion matrices
Matching algorithms to problems
Identifying unsafe or unethical scenarios
Eliminating implausible answers
Handling questions with multiple correct answers
Recognising common distractors

Guided Practice

AI terminology and concept questions
Python logic exercises
Data-quality scenarios
Accuracy, precision and recall calculations
Neural-network and deep-learning questions
Prompt-engineering scenarios
Generative AI and LLM evaluation
Responsible-AI case studies
Project-planning and communication questions

Mock Examination

Timed 36-question mock examination
Simulation of examination conditions
Review of questions and answers
Explanation of incorrect alternatives
Performance analysis by syllabus block
Identification of final revision priorities

Personal Preparation Plan

Individual instructor feedback
Targeted revision recommendations
Examination-day preparation
Time-management strategy
Follow-up practice and study resources