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AI+ Engineer
The AI+ Engineer certification programme offers a structured journey through the foundational principles,advanced techniques, and practical applications of Artificial Intelligence (AI).
Beginning with theFoundations of AI, participants progress through modules covering AI Architecture, Neural Networks,Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learningusing Hugging Face.
With a focus on hands-on learning, students develop proficiency in craftingsophisticated Graphical User Interfaces (GUIs) tailored for AI solutions and gain insight into AIcommunication and deployment pipelines. Upon completion, graduates are equipped with a robustunderstanding of AI concepts and techniques, ready to tackle real-world challenges and contributeeffectively to the ever-evolving field of Artificial Intelligence.
What You Will Learn
1. Foundations of Artificial Intelligence
Build a solid grounding in how AI works, including the history of the field, the differences between machine learning, deep learning and traditional programming, and the core mathematical and conceptual principles underpinning modern AI systems.
2. AI Architecture and the AI Development Lifecycle
Understand how AI systems are planned, designed, trained, and deployed. Explore the components of AI architecture, the role of high-quality data, and industry best practice across each stage of the lifecycle. Includes an introduction to ethical AI, bias, fairness and responsible system design.
Hands-on: Setting up your AI development environment using TensorFlow or PyTorch.
3. Neural Networks and Deep Learning
Explore the building blocks of neural networks—neurons, layers, activation functions, backpropagation, and optimisation algorithms. Learn how networks learn, adapt, and scale to different types of tasks.
Hands-on: Build a neural network for handwritten digit recognition (MNIST) and evaluate model performance.
4. Applying Neural Networks in Real-World Scenarios
Learn how deep learning models are used for image processing, computer vision, sequential data analysis, and time-series tasks.
Hands-on: Apply neural networks to both image and sequential datasets, and use transfer learning with pre-trained models.
5. Large Language Models (LLMs)
Gain a clear understanding of the role of LLMs in natural language understanding, including popular architectures such as BERT, GPT, and other transformer-based models. Explore their applications in classification, translation, chatbots, and sentiment analysis.
Hands-on: Fine-tune a pre-trained LLM for a real text-classification problem.
6. Generative AI (GANs & VAEs)
Discover how generative models work and how they are used to create synthetic, realistic data. Study GANs, Variational Autoencoders, and the challenges and opportunities they present for real-world engineering.
Hands-on: Implement and train a GAN for image generation.
7. Natural Language Processing (NLP) with Transformers
Examine advanced NLP techniques, including attention mechanisms and transformer-based models. Learn how NLP powers modern applications in sentiment analysis, conversational AI, and automated document processing.
Hands-on: Build end-to-end NLP pipelines using pretrained transformer models such as those available on Hugging Face.
8. Transfer Learning with Hugging Face
Explore best-practice approaches for transfer learning, including fine-tuning, feature extraction, and domain adaptation. See how pre-trained models can dramatically reduce development time while increasing performance.
Hands-on: Implement transfer learning for a range of tasks using Hugging Face models.
9. Building Sophisticated GUIs for AI Solutions
Learn how to package your AI models into user-friendly applications. Explore frameworks such as Streamlit, Dash, Tkinter, PyQt, PySide, and Electron for creating interactive web and desktop interfaces that make AI outputs accessible to non-technical users.
10. AI Communication and Deployment Pipeline
Develop the ability to communicate AI insights clearly to technical and non-technical audiences. Learn the stages of AI deployment, including CI/CD pipelines, model monitoring, and prototype development aligned to client requirements.
Entry Requirements
This is an advanced level certification targeted at students who have already completed the AI+Data or AI+Developer certification courses.
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.
The AI+ Engineer certification programme offers a structured journey through the foundational principles,advanced techniques, and practical applications of Artificial Intelligence (AI).
Beginning with theFoundations of AI, participants progress through modules covering AI Architecture, Neural Networks,Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learningusing Hugging Face.
With a focus on hands-on learning, students develop proficiency in craftingsophisticated Graphical User Interfaces (GUIs) tailored for AI solutions and gain insight into AIcommunication and deployment pipelines. Upon completion, graduates are equipped with a robustunderstanding of AI concepts and techniques, ready to tackle real-world challenges and contributeeffectively to the ever-evolving field of Artificial Intelligence.
What You Will Learn
1. Foundations of Artificial Intelligence
Build a solid grounding in how AI works, including the history of the field, the differences between machine learning, deep learning and traditional programming, and the core mathematical and conceptual principles underpinning modern AI systems.
2. AI Architecture and the AI Development Lifecycle
Understand how AI systems are planned, designed, trained, and deployed. Explore the components of AI architecture, the role of high-quality data, and industry best practice across each stage of the lifecycle. Includes an introduction to ethical AI, bias, fairness and responsible system design.
Hands-on: Setting up your AI development environment using TensorFlow or PyTorch.
3. Neural Networks and Deep Learning
Explore the building blocks of neural networks—neurons, layers, activation functions, backpropagation, and optimisation algorithms. Learn how networks learn, adapt, and scale to different types of tasks.
Hands-on: Build a neural network for handwritten digit recognition (MNIST) and evaluate model performance.
4. Applying Neural Networks in Real-World Scenarios
Learn how deep learning models are used for image processing, computer vision, sequential data analysis, and time-series tasks.
Hands-on: Apply neural networks to both image and sequential datasets, and use transfer learning with pre-trained models.
5. Large Language Models (LLMs)
Gain a clear understanding of the role of LLMs in natural language understanding, including popular architectures such as BERT, GPT, and other transformer-based models. Explore their applications in classification, translation, chatbots, and sentiment analysis.
Hands-on: Fine-tune a pre-trained LLM for a real text-classification problem.
6. Generative AI (GANs & VAEs)
Discover how generative models work and how they are used to create synthetic, realistic data. Study GANs, Variational Autoencoders, and the challenges and opportunities they present for real-world engineering.
Hands-on: Implement and train a GAN for image generation.
7. Natural Language Processing (NLP) with Transformers
Examine advanced NLP techniques, including attention mechanisms and transformer-based models. Learn how NLP powers modern applications in sentiment analysis, conversational AI, and automated document processing.
Hands-on: Build end-to-end NLP pipelines using pretrained transformer models such as those available on Hugging Face.
8. Transfer Learning with Hugging Face
Explore best-practice approaches for transfer learning, including fine-tuning, feature extraction, and domain adaptation. See how pre-trained models can dramatically reduce development time while increasing performance.
Hands-on: Implement transfer learning for a range of tasks using Hugging Face models.
9. Building Sophisticated GUIs for AI Solutions
Learn how to package your AI models into user-friendly applications. Explore frameworks such as Streamlit, Dash, Tkinter, PyQt, PySide, and Electron for creating interactive web and desktop interfaces that make AI outputs accessible to non-technical users.
10. AI Communication and Deployment Pipeline
Develop the ability to communicate AI insights clearly to technical and non-technical audiences. Learn the stages of AI deployment, including CI/CD pipelines, model monitoring, and prototype development aligned to client requirements.
Entry Requirements
This is an advanced level certification targeted at students who have already completed the AI+Data or AI+Developer certification courses.
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.