An Interactive 5-Day Training Course

An Interactive 5-Day Training Course

AI+ Engineer Certification Program

AI+ Engineer Certification Program

AI+ Engineer Certification Program

Master AI Architecture, Neural Networks, NLP, and Generative AI for Engineering Applications

Master AI Architecture, Neural Networks, NLP, and Generative AI for Engineering Applications

Description

31st Aug - 4th Sep 2026

Barcelona, Spain

$4,950

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.

Certification Target

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 

AI+ Engineer Certification Program

Course Outline:

Day 1

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 

Day 1

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 

Day 1

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 

Day 2

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) 

Day 2

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) 

Day 2

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) 

Day 3

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 

Day 3

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 

Day 3

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 

Day 4

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 

Day 4

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 

Day 4

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 

Day 5

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 

Day 5

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 

Day 5

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 

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 

Customisation & Delivery Options

Ideal for: 


  • Public enrolment 

  • Corporate teams (customisable schedule and labs) 

  • Industry-specific adaptations (finance, government, healthcare) 

Get the AI+ Engineer Certification Program Outline:

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.