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AI+ Engineer Certification Program
Master AI Architecture, Neural Networks, NLP, and Generative AI for Engineering Applications
Welcome to our AI+ Engineer Certification Program, a structured, hands-on course covering AI foundations, neural networks, LLMs, NLP, Generative AI, and deployment for AI engineering professionals.
Includes:
Hands-on Labs, Real-World Projects, Capstone Exercise & AI CERTs Certification Preparation.
Course fee
$4,950
per participant, incl. exam voucher where applicable
This course prepares learners for the AI CERTs™ AI+ Engineer certification, a globally recognised credential validating proficiency in AI engineering, neural networks, NLP, and Generative AI deployment.
Course Overview
The AI+ Engineer certification program offers a structured journey through the foundational principles, advanced techniques, and practical applications of Artificial Intelligence. Beginning with the Foundations of AI, participants progress through modules covering AI Architecture, Neural Networks, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learning.
With a focus on hands-on learning, students develop proficiency in crafting AI solutions and gain insight into AI communication and deployment pipelines.
Instructor
We bring you top-tier global AI experts as instructors, professionals who combine:
Strong academic backgrounds from leading international universities
Extensive industry experience across multiple sectors
Deep specialisation in AI, machine learning, and data science
Multilingual communication abilities, ensuring clarity for diverse audiences
Who Should Attend?
Software engineers and developers
Data scientists seeking deeper AI engineering skills
ML engineers and technical leads
Solution architects designing AI systems
Anyone seeking a structured AI engineering certification
Prerequisites
AI+ Data or AI+ Developer course (recommended)
Basic understanding of Python programming
Familiarity with high school-level algebra and basic statistics
Computer science fundamentals (variables, functions, data structures)
Learning Objectives
Upon completion, participants will be able to:
Design and implement AI architectures for real-world applications
Build and train neural networks including CNNs and RNNs
Fine-tune Large Language Models for domain-specific tasks
Apply NLP techniques including transformers and attention mechanisms
Deploy AI models with proper documentation and production pipelines
Organisational Impact
Building AI engineering capability creates transformative organisational value:
• Accelerate AI product development with skilled engineering teams
• Build custom AI solutions tailored to specific business needs
• Reduce dependency on third-party AI vendors
• Improve AI model quality, reliability, and deployment speed
• Establish best practices for responsible AI engineering
Customisation & Delivery Options
Ideal for:
Public enrolment
Corporate teams (customisable schedule and labs)
Industry-specific adaptations (finance, government, healthcare)
Exam Information
This course prepares participants for the AI CERTs™ AI+ Engineer certification exam. The exam validates proficiency in AI architecture, neural networks, LLMs, NLP, Generative AI, and AI deployment practices.
What's Included
Expert-led instruction
Hands-on labs with TensorFlow, PyTorch, and Hugging Face
Capstone project: End-to-end AI engineering pipeline
Digital courseware and resources
AI CERTs™ certification exam voucher
Curriculum
The 5-day outline.
Every day combines instruction with hands-on labs. You leave having done it, not just heard it.
Foundations of AI & Architecture
Introduction to AI: history, core concepts, ethical considerations
Machine Learning fundamentals: supervised, unsupervised, reinforcement learning
AI Architecture: key components, development lifecycle, best practices
Lab: Setting up an AI environment with TensorFlow and PyTorch
Neural Networks & Deep Learning
Neural network foundations: neurons, layers, activation functions
Backpropagation and optimisation algorithms (Gradient Descent, Adam, RMSprop)
Applications: image processing, sequential data, transfer learning
Lab: Building a neural network for handwritten digit recognition (MNIST)
Large Language Models & Generative AI
Understanding LLMs: BERT, GPT, and their real-world applications
Practical finetuning of language models for domain-specific tasks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Lab: Finetuning a language model for text classification
Natural Language Processing & Transfer Learning
NLP in real-world scenarios: sentiment analysis, chatbots, translation
Attention mechanisms and transformer architectures
BERT for practical NLP tasks and transfer learning applications
Lab: Building an NLP pipeline with Hugging Face
AI Deployment, GUI Development & Capstone
Building AI-powered graphical user interfaces
AI communication and deployment pipelines
Model packaging, documentation, and production handoff
Capstone: End-to-end AI engineering project from model to deployment
More dates
Same course. Other dates & cities.
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