An Interactive 5-Day Training Course

An Interactive 5-Day Training Course

AI+ Developer Certification Program

AI+ Developer Certification Program

AI+ Developer Certification Program

Master Python, Machine Learning, Deep Learning, and AI Application Development

Master Python, Machine Learning, Deep Learning, and AI Application Development

Description

5th - 9th Oct 2026

Barcelona, Spain

$4,950

Welcome to our AI+ Developer Certification Program — a comprehensive, hands-on course covering Python, mathematics, machine learning, deep learning, NLP, computer vision, and AI application deployment.

Includes:
Hands-on Labs, Real-World Projects, Capstone Exercise & AI CERTs Certification Preparation.

Certification Target

This course prepares learners for the AI CERTs™ AI+ Developer certification — a globally recognised credential validating proficiency in AI development, machine learning, deep learning, and AI deployment. 

Course Overview

The AI+ Developer certification program offers a tailored journey in key AI domains for developers. Master Python, advanced concepts, math, stats, optimisation, and deep learning. The curriculum covers data processing, exploratory analysis, and allows specialisation in NLP, computer vision, or reinforcement learning. 

The program includes time series analysis, model explainability, and deployment intricacies. This course also covers AI Agents for Developers, including GitHub Copilot, CI/CD automation, and practical agent development. 

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 developers and programmers 


  • Junior to mid-level data scientists 


  • Technical professionals transitioning to AI development 


  • Computer science graduates seeking applied AI skills 


  • Anyone seeking a structured AI developer certification 

Prerequisites

  • Familiarity with high school-level algebra and basic statistics 


  • Computer science fundamentals (variables, functions, loops, data structures) 


  • Proficiency in Python is mandatory for hands-on exercises and project work 

Learning Objectives

Upon completion, participants will be able to: 


  • Build and deploy machine learning models using Python 


  • Implement deep learning architectures (CNNs, RNNs, LSTMs) for real-world tasks 


  • Apply NLP techniques for text classification, NER, and question answering 


  • Build computer vision applications including object detection 


  • Deploy AI applications using cloud platforms (AWS, Azure, GCP) 

Organisational Impact

Developing AI development capability creates significant organisational value: 

• Build custom AI applications tailored to specific business needs 

• Accelerate product innovation with AI-powered features 

• Reduce time-to-market for AI solutions with skilled developers 

• Leverage AI agents for code review, testing, and CI/CD automation 

• Establish scalable AI development practices across engineering teams 

AI+ Developer Certification Program

Course Outline:

Day 1

AI Foundations & Mathematical Concepts
  • Introduction to AI: history, types, branches, and business applications 


  • Mathematical foundations: linear algebra, calculus, probability, and statistics 


  • Discrete mathematics: sets, logic, graph theory, combinatorics 


  • Lab: Mathematical problem-solving for AI applications 

Day 1

AI Foundations & Mathematical Concepts
  • Introduction to AI: history, types, branches, and business applications 


  • Mathematical foundations: linear algebra, calculus, probability, and statistics 


  • Discrete mathematics: sets, logic, graph theory, combinatorics 


  • Lab: Mathematical problem-solving for AI applications 

Day 1

AI Foundations & Mathematical Concepts
  • Introduction to AI: history, types, branches, and business applications 


  • Mathematical foundations: linear algebra, calculus, probability, and statistics 


  • Discrete mathematics: sets, logic, graph theory, combinatorics 


  • Lab: Mathematical problem-solving for AI applications 

Day 2

Python for AI Development
  • Python fundamentals: syntax, control flow, data structures, modules 


  • Essential libraries: NumPy for numerical computing, Pandas for data analysis 


  • Data visualisation with Matplotlib and Seaborn 


  • Lab: Data analysis and visualisation pipeline with Python 

Day 2

Python for AI Development
  • Python fundamentals: syntax, control flow, data structures, modules 


  • Essential libraries: NumPy for numerical computing, Pandas for data analysis 


  • Data visualisation with Matplotlib and Seaborn 


  • Lab: Data analysis and visualisation pipeline with Python 

Day 2

Python for AI Development
  • Python fundamentals: syntax, control flow, data structures, modules 


  • Essential libraries: NumPy for numerical computing, Pandas for data analysis 


  • Data visualisation with Matplotlib and Seaborn 


  • Lab: Data analysis and visualisation pipeline with Python 

Day 3

Machine Learning: Theory to Practice
  • Supervised ML: regression and classification (logistic, SVM, random forests) 


  • Unsupervised ML: clustering (k-means, hierarchical) and dimensionality reduction (PCA, t-SNE) 


  • Model evaluation, cross-validation, and selection 


  • Lab: Building classification and clustering models on real-world datasets 

Day 3

Machine Learning: Theory to Practice
  • Supervised ML: regression and classification (logistic, SVM, random forests) 


  • Unsupervised ML: clustering (k-means, hierarchical) and dimensionality reduction (PCA, t-SNE) 


  • Model evaluation, cross-validation, and selection 


  • Lab: Building classification and clustering models on real-world datasets 

Day 3

Machine Learning: Theory to Practice
  • Supervised ML: regression and classification (logistic, SVM, random forests) 


  • Unsupervised ML: clustering (k-means, hierarchical) and dimensionality reduction (PCA, t-SNE) 


  • Model evaluation, cross-validation, and selection 


  • Lab: Building classification and clustering models on real-world datasets 

Day 4

Deep Learning & Computer Vision
  • Neural networks: perceptrons, activation functions, deep learning frameworks 


  • CNNs for image classification, RNNs/LSTMs for sequential data 


  • Computer vision: image processing, object detection (YOLO, SSD), segmentation 


  • Lab: Building an image classification model and object detection app 

Day 4

Deep Learning & Computer Vision
  • Neural networks: perceptrons, activation functions, deep learning frameworks 


  • CNNs for image classification, RNNs/LSTMs for sequential data 


  • Computer vision: image processing, object detection (YOLO, SSD), segmentation 


  • Lab: Building an image classification model and object detection app 

Day 4

Deep Learning & Computer Vision
  • Neural networks: perceptrons, activation functions, deep learning frameworks 


  • CNNs for image classification, RNNs/LSTMs for sequential data 


  • Computer vision: image processing, object detection (YOLO, SSD), segmentation 


  • Lab: Building an image classification model and object detection app 

Day 5

NLP, LLMs, Reinforcement Learning & Cloud AI
  • NLP: text preprocessing, classification, NER, question answering 


  • LLMs: architecture, fine-tuning, text generation, and knowledge extraction 


  • Reinforcement learning fundamentals and cloud-based AI deployment 


  • Capstone: Building and deploying an AI application using cloud services 

Day 5

NLP, LLMs, Reinforcement Learning & Cloud AI
  • NLP: text preprocessing, classification, NER, question answering 


  • LLMs: architecture, fine-tuning, text generation, and knowledge extraction 


  • Reinforcement learning fundamentals and cloud-based AI deployment 


  • Capstone: Building and deploying an AI application using cloud services 

Day 5

NLP, LLMs, Reinforcement Learning & Cloud AI
  • NLP: text preprocessing, classification, NER, question answering 


  • LLMs: architecture, fine-tuning, text generation, and knowledge extraction 


  • Reinforcement learning fundamentals and cloud-based AI deployment 


  • Capstone: Building and deploying an AI application using cloud services 

Exam Information

This course prepares participants for the AI CERTs™ AI+ Developer certification exam. The exam validates proficiency in Python, machine learning, deep learning, NLP, computer vision, and AI application deployment.

What's Included

  • Expert-led instruction 


  • Hands-on labs covering ML, DL, NLP, and computer vision 


  • Capstone project: Cloud-deployed AI application 


  • 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+ Developer 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.