AI+Developer Certification

Opportunities Workshop

AI+Developer

Digital certificate - Included

AI+Developer

Included in course price

• Detailed Curriculum - download

The AI+ Developer Certification provides a structured, comprehensive pathway for those who want to develop real technical capability in Artificial Intelligence. The programme progresses from core foundations to advanced modelling techniques, aligning with the workflows used in industry-level AI development.

Learners begin by exploring:

• The evolution of AI and its major milestones

• Definitions of intelligence in computational contexts

• The spectrum from Narrow AI to emerging general-purpose systems

• Functional types of AI including reactive systems, limited-memory models, and self-aware architectures

• Key branches such as Machine Learning, Deep Learning, Generative AI, Computer Vision, NLP, robotics, and expert systems

Case studies demonstrate how organisations employ AI to improve efficiency, support decision-making, and solve complex problems.

A strong mathematical base is essential for understanding and optimising AI models. Modules cover:

Linear Algebra – matrices, vectors, eigenvalues, transformations, and the operations that underpin neural network computation.
Calculus – gradients, derivatives, integrals, and optimisation functions that allow models to learn.
Probability & Statistics – distributions, sampling, hypothesis testing, Bayesian inference, and reasoning under uncertainty.
Discrete Mathematics – logic, sets, graphs, and combinatorics for algorithmic thinking.

Practical exercises help learners link mathematical concepts directly to model behaviour.

Participants strengthen their programming capability through modules on:

• Python fundamentals, data structures, and modular programming

• NumPy for numerical computation

• Pandas for data manipulation

• Matplotlib/Seaborn for visual exploration and reporting

These skills support efficient data preparation and model implementation.

The programme covers the full machine learning pipeline:

• Data preprocessing and feature engineering

• Supervised learning algorithms (regression, decision trees, SVMs, ensemble methods)

• Unsupervised techniques (clustering, anomaly detection, dimensionality reduction)

• Model comparison, k-fold validation, and performance metrics

• Hands-on projects such as stock prediction, sentiment analysis, customer segmentation, and classification tasks

Learners gain experience selecting, training, and refining models for specific use cases.

This advanced section explores:

• The structure of artificial neural networks

• Backpropagation and weight optimisation

• CNNs for image classification and object detection

• RNNs, LSTMs, and GRUs for sequential and text-based tasks

• GANs for image synthesis and style transformation

Participants build end-to-end models including handwriting recognisers, image classifiers, sentiment analysers, segmentation networks, and object detection systems.

Specialised modules cover:

• Image representations, transformations, and feature extraction

• Convolutional pipelines

• Object detection methods such as YOLO and SSD

• Image segmentation (semantic, instance, and U-Net-based approaches)

• Applications in healthcare imaging, autonomous navigation, recommendation systems, and industrial automation

Hands-on labs help learners tackle real-world image challenges.

Participants explore how models understand, generate, and analyse text:

• Tokenisation, stemming, lemmatisation, and embeddings

• Text classification, topic modelling, and sentiment analysis

• Named Entity Recognition for extracting structured information

• Question-answering systems using models such as BERT and T5

• Practical exercises using social media posts, customer feedback, and long-form documents

This section covers:

• Agents, states, actions, policies, and reward functions

• Q-Learning and Deep Q-Networks

• Policy-gradient methods for continuous control

• Building custom environments

• Practical projects such as navigating mazes or controlling simple robots

Learners develop the ability to:

• Configure cloud-based AI environments

• Use AutoML and pre-trained services

• Deploy models using cloud APIs

• Build scalable AI applications using AWS, Azure, or GCP

• Deliver end-to-end project solutions from training through to deployment

The programme closes with a forward-looking set of modules:

• Foundations of LLM architecture and training

• Applying LLMs for generation, translation, summarisation, and retrieval

• Knowledge extraction and question-answering pipelines

• Explainable AI and model transparency techniques

• Federated learning for privacy-preserving training

• Neuro-symbolic approaches for reasoning

• Meta-learning and few-shot approaches for rapid adaptation

Participants also learn how to communicate results clearly through documentation, presentations, and model explainability reports.

This programme is ideal for learners who want to build hands-on technical capability in AI development. It is suited to:

• Aspiring AI engineers and junior developers

• Software developers expanding into AI and ML

• Data analysts progressing into model-building roles

• Technical professionals wanting structured, practical AI skills

• Engineers working in robotics, automation, or digital product development

• Students preparing for graduate-level roles in AI or Data Science

• Professionals seeking to understand modern AI tools to complement existing development skills

The course welcomes participants from a variety of technical backgrounds, provided they are comfortable with Python and core computing concepts.

Participants should have basic Python programming experience and a working knowledge of core mathematical concepts. Understanding variables, loops, functions, and common data structures is essential. A willingness to engage with hands-on coding and model-building exercises will support successful progression.

Graduates are prepared for roles such as AI Developer, Machine Learning Engineer, Computer Vision Engineer, NLP Engineer, Data Scientist (entry-level), ML Ops Assistant, or AI Application Developer.