AI+ Developer

AI+ Developer certification programme offers a tailored journey in key AI domains for developers. MasterPython, advanced concepts, maths, stats, optimisation, and deep learning. The curriculum covers dataprocessing, exploratory analysis, and allows specialization in NLP, computer vision, or reinforcementlearning. The programme includes time series analysis, model explainability, and deployment intricacies. Upon completion, you'll receive a certification, showcasing your AI proficiency for real-world challenges.

What You Will Learn

1. Foundations of Artificial Intelligence

Gain a clear understanding of what AI is, how it has evolved, and how different branches—including machine learning, deep learning, computer vision, robotics, and NLP—contribute to real-world solutions. Explore the different types of AI (Narrow, General, Super AI) and the functional categories of AI such as reactive machines, theory of mind, and self-awareness.

2. Mathematical Concepts for AI

Develop the mathematical grounding needed for modern AI development, including:

• Linear Algebra – vectors, matrices, eigenvalues, transformations.

• Calculus – derivatives, gradients, optimisation.

• Probability & Statistics – distributions, hypothesis testing, Bayesian inference.

• Discrete Mathematics – sets, logic, combinatorics, graph theory.

These concepts underpin model design, optimisation, and data processing workflows throughout the programme.

3. Python for AI Development

Strengthen Python skills required for AI engineering, including control flow, data structures, modules, and packages. Work with essential AI libraries:

• NumPy for numerical computing

• Pandas for data analysis

• Matplotlib & Seaborn for data visualisation

Participants build clean, efficient code for data processing and modelling pipelines.

4. Mastering Machine Learning

Explore the core machine learning disciplines:

• Supervised Learning – regression, classification, decision trees, SVMs.

• Unsupervised Learning – clustering (K-Means, hierarchical), dimensionality reduction (PCA, t-SNE).

• Reinforcement Learning fundamentals – introduction to agent-based learning.

• Model Evaluation & Selection – metrics, cross-validation, best-fit modelling.

Hands-on projects include stock prediction, sentiment analysis, and segmentation tasks.

5. Deep Learning

Master neural networks and cutting-edge deep learning architectures:

• Neural Networks – perceptrons, feedforward models, activation functions.

• CNNs – image recognition, advanced architectures (LeNet, AlexNet, VGG, ResNet).

• RNNs – LSTMs and GRUs for sequential and time-series data.

• GANs – image generation and style transformation.

Practical tasks include digit recognition, image classification, and text-based generative modelling.

6. Computer Vision

Build expertise in image-based AI applications:

• Image processing and transformations

• Object detection with YOLO, SSD

• Semantic and instance segmentation using U-Net

• Real-world applications in medical imaging, robotics, and autonomous systems

Hands-on exercises include building a real-time object detection app and segmentation pipelines.

7. Natural Language Processing (NLP)

Learn the full NLP workflow—from cleaning and preparing text to building advanced models:

• Tokenisation, stemming, lemmatisation

• Word embeddings

• Text classification (sentiment, topics, spam detection)

• Named Entity Recognition (NER)

• Question Answering with models such as BERT and T5

Hands-on projects include sentiment analysers, information extractors, and question-answering systems.

8. Reinforcement Learning

Gain practical experience with RL through:

• Q-learning and Deep Q-Networks (DQNs)

• Policy gradient methods and actor-critic models

• Real-world control tasks and game-based learning

Participants train agents to play simple games, navigate environments, and perform control tasks.

9. Cloud Computing for AI

Learn how to use cloud platforms (AWS, Azure, GCP) for scalable AI development, including:

• Cloud-based environment setup

• Accessing pre-trained models and AutoML tools

• Deploying AI applications on cloud services

Hands-on: Build an end-to-end AI application using cloud tools.

10. Large Language Models (LLMs)

Understand the architecture, training processes, and practical applications of LLMs. Explore capabilities such as:

• Text generation

• Translation

• Knowledge extraction

• Retrieval-augmented question answering

Hands-on sessions use open-source LLMs to build practical applications.

11. Emerging Research & Advanced Concepts

• Explore next-generation AI research and methodologies:

• Neuro-Symbolic AI – combining logic and deep learning

• Federated Learning – privacy-preserving distributed training

• Explainable AI (XAI) – interpreting and explaining model behaviour

• Meta-Learning & Few-Shot Learning – enabling models to learn with minimal data

12. AI Communication, Documentation & Ethics

Develop the professional skills to:

• Communicate AI projects to technical and non-technical audiences

• Write clear and maintainable project documentation

• Apply ethical principles—fairness, transparency, accountability—to AI workflows

Hands-on exercises include preparing presentations, writing AI documentation, and evaluating ethical risks.

Entry Requirements

This is a practitioner level certification targeted at students with existing technical knowledge. The AI+ Developer programme is ideal for software developers, analysts, and engineers who want to build deep competence in the practical development of AI systems

Participants should possess the following baseline skills:

• Programming in Python for hands-on lab activities and project work

• Understanding of key algebra and statitstical concepts.

• Understanding of basic programming concepts (variables, functions,loops) and data structures (lists, dictionaries).

Typical completion time

5 days / 40 hours

Self-paced online learning with videos, podcasts, practical lab activities and Q&A tutor chat.

Coaching option

Includes 3 × 45 minutes live coaching sessions with our AI Certs Certified Trainers to fully prepare student for the certification exam.