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AI+ Developer Certification Program
Master Python, Machine Learning, Deep Learning, and AI Application Development
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.
Course fee
$4,950
per participant, incl. exam voucher where applicable
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
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+ 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
Curriculum
The 5-day outline.
Every day combines instruction with hands-on labs. You leave having done it, not just heard it.
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
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
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
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
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
More dates
Same course. Other dates & cities.
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