Artificial Intelligence and Applied Gen AI Certification in Dubai
90 hours of AI, GenAI & Agentic Learning training program
Globally recognised AI certification with LLMs & RAG Systems
Learn LangChain, CrewAI & LangGraph applications
35 modules, AI simulation & real-world deployment
Flexible learning options with easy instalments
4.8/54.8/5Overview
What will you learn from us:
- Master Python, Machine Learning, Deep Learning, LLMs, and Generative AI through a structured 90-hour learning journey
- Build NLP applications, AI assistants, chatbots, and enterprise-ready LLM solutions
- Develop Retrieval-Augmented Generation (RAG) applications using LangChain and vector databases
- Create Generative AI and Multimodal AI solutions using Transformers, GANs, and Diffusion Models
- Design autonomous AI agents and multi-agent workflows using CrewAI and LangGraph
- Apply AI to business challenges through predictive analytics, Power BI, FastAPI, and enterprise AI deployment
- Prepare for production-ready AI, Generative AI, and AI Automation roles through practical projects and simulations
- Progress through Foundations → Data Science → Machine Learning → Deep Learning → Generative AI → LLM Engineering → Agentic AI & Autonomous Systems
Upcoming sessions
Curriculum
What is AI, ML, and GenAI
AI vs Human Intelligence
History and Evolution of AI
Current AI Tools Landscape
Real-World Business Applications
ChatGPT, Claude, Gemini Overview
AI Integration
- Compare AI platforms for different business use cases
- Evaluate AI adoption opportunities across departments and business functions
Activities/Case Study
How Organizations Are Using AI to Improve Productivity, Customer Experience, and Decision-Making
Introduction to Claude.ai Interface
Prompt Engineering Fundamentals
Writing Effective Prompts for Business
Document Summarization & Drafting
Claude for Research and Analysis
Claude vs ChatGPT Practical Comparison
AI Integration
- Create role-specific AI workflows for business productivity
- Utilize Claude for research, content creation, documentation, and analytical support
Activities/Case Study
Automating Meeting Summaries, Research Notes, and Email Drafts Using Claude
Python Syntax, Variables, Data Types
Conditional Statements and Loops
Functions and Lambda Functions
Lists, Tuples, Dictionaries, Sets
Automating Data Workflows
AI Integration
- Generate Python scripts for analytical workflows
- Automate repetitive data processing activities
Activities/Case Study
Business Automation with Python for Management Reporting Processes
Jupyter/Colab Usage
NumPy Arrays and Indexing
Vectorized Operations
Matrix Transformations
Numerical Computation Workflows
AI Integration
- Accelerate numerical analysis through AI-assisted coding
- Optimize analytical computations and workflows
Activities/Case Study
Logistics Planning and Resource Allocation Using NumPy
Data Loading and Cleaning
Handling Missing Values
Data Merging
Data Transformation
Dataset Preparation Workflows
AI Integration
- Automate data cleaning and transformation processes
- Generate dataset preparation recommendations
Activities/Case Study
Consolidating Departmental Reports into a Unified Dataset
Statistical Measures
Mean, Median, Mode
Variance and Standard Deviation
Distribution Analysis
Probability Rules
Normal Distribution
Binomial Distribution
Business Data Interpretation
AI Integration
- Interpret statistical outputs and trends
- Generate business-focused analytical insights
Activities/Case Study
Statistical Performance Analysis for Workforce and Sales Data
Central Limit Theorem
Confidence Intervals
t-Tests
Chi-Square Tests
ANOVA
A/B Testing
Decision-Making Using Statistics
AI Integration
- Validate statistical assumptions and outcomes
- Support evidence-based business decisions
Activities/Case Study
Product Feature Evaluation Using Hypothesis Testing
Histograms and Bar Charts
Boxplots and Heatmaps
Correlation Analysis
Visualization Best Practices
Dashboard-Ready Charts
AI Integration
- Recommend effective visualization approaches
- Enhance storytelling through AI-generated insights
Activities/Case Study
Retail Performance Visualization and Trend Analysis
Feature Engineering
Scaling and Encoding
Outlier Detection
Data Exploration Workflows
Pattern Identification
AI Integration
- Identify patterns and hidden relationships
- Support feature engineering and exploration activities
Activities/Case Study
Sales and Finance Data Exploration for Trend Identification
Machine Learning Types and Workflows
Model Training and Evaluation
Performance Metrics
Model Validation
Business ML Applications
AI Integration
- Compare machine learning approaches and use cases
- Interpret model performance and outcomes
Activities/Case Study
Workforce Retention Analysis Using Machine Learning
Linear Regression
Logistic Regression
Model Interpretation
ROC-AUC
Confusion Matrix
Prediction Systems
AI Integration
- Evaluate predictive model effectiveness
- Generate prediction-focused business insights
Activities/Case Study
Sales Forecasting and Revenue Prediction
Decision trees
Random forests
KNN algorithm
Model tuning
Predictive modeling use cases
AI Integration
- Compare classification model performance
- Improve predictive decision-making capability
Activities/Case Study
Customer Risk Assessment and Classification Analysis
K-means clustering
Hierarchical clustering
PCA
Feature reduction
Segmentation models
AI Integration
- Generate customer segmentation insight
- Support pattern discovery and clustering analysis
Activities/Case Study
Market Segmentation and Customer Profiling Analysis
Data Integration
Power Query Transformations
Visual Dashboards
KPI Tracking
Reporting Workflows
Data Modeling
DAX Calculations
Multi-Page Dashboards
Forecasting Models
Dashboard Optimization
AI Integration
- Enhance dashboard insights using AI recommendations
- Automate KPI monitoring and reporting analysis
- Improve forecasting accuracy through AI insights
- Optimize dashboards for executive decision-making
Activities/Case Study
HR Attrition Dashboard Finance Dashboard with Forecasting
Model Export and Integration
Dashboard Automation
Predictive Analytics Visualization
Business Intelligence Workflows
Data Storytelling
AI Integration
- Integrate predictive models into dashboards
- Generate actionable business recommendations
Activities/Case Study
Telecom Churn Prediction and Reporting Solution
Artificial Neural Network Concepts
Activation Functions
Forward Propagation
Backward Propagation
Keras Implementation
Neural Network Design
AI Integration
- Design neural network architectures efficiently
- Optimize model structure and performance
Activities/Case Study
Customer Churn Prediction Using ANN Models
TensorFlow and Keras
Loss Functions and Optimizers
Overfitting and Underfitting
Model Tuning
Training Workflows
AI Integration
- Improve training and tuning workflows
- Optimize neural network performance
Activities/Case Study
Sales Forecasting Using Deep Learning Models
Text preprocessing
Tokenization and lemmatization
Vectorization techniques
NLP workflows
Sentiment analysis
AI Integration
- Automate text analysis workflows
- Extract insights from unstructured data
Activities/Case Study
Customer Feedback Analysis Using NLP Techniques
Logistic regression and Naive Bayes
Word embeddings
Classification models
Text categorization
Model evaluation
AI Integration
- Improve text classification accuracy
- Generate NLP-driven business insights
Activities/Case Study
Email Categorization and Support Ticket Routing
Image processing
CNN architecture
Transfer learning
Model storage
Deployment basics
AI Integration
- Build image recognition solutions
- Accelerate computer vision implementations
Activities/Case Study
Product Defect Detection Using Computer Vision
Flask and FastAPI
REST API development
Model deployment
Integration workflows
Production considerations
AI Integration
- Deploy AI models into production systems
- Streamline API-based AI integration
Activities/Case Study
AI-Powered Recommendation Service Deployment
Reinforcement Learning Concepts
Q-Learning
Agent-Environment Interaction
Exploration vs Exploitation
Simulation Models
AI Integration
- Develop intelligent decision agents
- Optimize learning and simulation workflows
Activities/Case Study
Warehouse Optimization Through Reinforcement Learning
Self-Attention Mechanisms
Transformer Architecture
BERT vs GPT
Positional Encoding
Text Generation
AI Integration
- Build transformer-based AI solutions
- Improve language model understanding
Activities/Case Study
Enterprise Document Summarization Using Transformer Architecture
GAN Architecture
Generator and Discriminator
GAN Variants
Image Generation
Model Training
AI Integration
- Create AI-generated visual content
- Improve generative model performance
Activities/Case Study
Marketing Content Creation Using GAN-Generated Visuals
Diffusion Process
DDPM
Stable Diffusion Tools
Image Generation Workflows
Model Comparison
AI Integration
- Generate high-quality AI-created media
- Compare generative model approaches
Activities/Case Study
Creative Asset Production Using Diffusion Models
GPT and LLM concepts
Prompt engineering
Fine-tuning techniques
Domain-specific models
Chatbot development
AI Integration
- Develop domain-specific AI assistants
- Optimize prompts and model performance
Activities/Case Study
Customer Support Chatbot Development Using GPT Models
LangChain Architecture
Vector Databases
Retrieval-Augmented Generation
Knowledge Integration
AI Pipelines
AI Integration
- Build enterprise RAG solutions
- Connect LLMs with organizational knowledge
Activities/Case Study
Enterprise Policy and Document Retrieval Solution
Vision-Language Models
Media Generation
Multimodal Systems
AI Creativity Tools
Application Design
AI Integration
- Develop multimodal AI experiences
- Integrate text, image, and media intelligence
Activities/Case Study
AI-Powered Multimedia Marketing Solution
Multi-Agent Systems
Autonomous Workflows
Decision Agents
Task Orchestration
AI Assistants
AI Integration
- Design autonomous AI workflows
- Build intelligent decision-making agents
Activities/Case Study
Automated Customer Support Orchestration System
Multi-Agent Collaboration
LangChain Dynamic Decision Agents
Autonomous Assistant Architecture
Agent Communication
Collaborative Workflows
AI Integration
- Enable agent collaboration and coordination
- Develop adaptive AI assistant workflows
Activities/Case Study
Cross-Functional Business Process Automation Using Agents
Task Decomposition and Planning
Reflex vs Learning Agents
CrewAI Orchestration
LangGraph Fundamentals
Multi-Agent Workflow Design
Agent Collaboration Strategies
AI Integration
- Orchestrate complex AI task execution
- Build scalable multi-agent workflows
Activities/Case Study
Autonomous Project Management and Task Coordination
LangChain Tools
Search Tool Integration
API Connectivity
Zapier Integrations
Agent Memory Architectures
Vector Memory
Long-Term Memory
Summary Memory
Context Retention Strategies
Web-Aware Chatbots
AI Integration
- Enhance agents with memory and external tools
- Improve contextual reasoning and long-term task management
Activities/Case Study
Enterprise Knowledge Assistant with Persistent Memory
Function Calling Fundamentals
ReAct Prompting Framework
Toolformer Concepts
Tool-Based Reasoning
Dynamic Tool Selection
AI Decision-Making Workflows
AI Integration
- Enable intelligent tool-based reasoning
- Improve AI agent decision accuracy and adaptability
Activities/Case Study
AI Assistant Performing Dynamic Tool Selection for Business Queries
Prompt → Tools → Output Orchestration
Agent Workflow Management
Guardrails and Safety Controls
Observability and Monitoring
Evaluation Metrics
Production Workflows
Enterprise Deployment Considerations
AI Integration
- Deploy autonomous AI systems safely and effectively
- Monitor and evaluate AI agent performance in production environments
Activities/Case Study
Enterprise AI Operations and Observability Implementation
AI Ethics and Bias
Responsible AI Principles
Model Governance
AI Risk Management
Deployment Platforms
Monitoring and Evaluation
Compliance Requirements
Public Hosting Considerations
AI Integration
- Implement responsible AI practices
- Strengthen governance, compliance, and risk management controls
Activities/Case Study
Responsible AI Deployment Review for Enterprise Applications
Participants assume the role of an Enterprise Artificial Intelligence Center of Excellence (AI CoE) tasked with leading a large-scale AI transformation initiative across multiple business functions.
The organization seeks to improve operational efficiency, automate knowledge-intensive processes, strengthen forecasting accuracy, enhance customer experience, optimize workforce productivity, and accelerate decision-making through Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI technologies.
Working within a complex business environment, participants evaluate organizational challenges, identify AI implementation opportunities, prepare and analyze data, develop predictive models, build Generative AI solutions, design Retrieval-Augmented Generation (RAG) architectures, create autonomous agent workflows, and integrate business intelligence capabilities into enterprise decision-making processes.
The simulation integrates the complete AI lifecycle from business problem identification and data preparation through machine learning development, deep learning implementation, Generative AI deployment, autonomous agent orchestration, dashboard integration, governance assessment, and executive reporting.
Participants apply Artificial Intelligence, Data Science, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Agentic AI, and Business Intelligence methodologies to deliver scalable AI solutions, intelligent automation strategies, predictive insight frameworks, and enterprise transformation roadmaps that align with organizational objectives while ensuring responsible AI adoption, governance compliance, operational resilience, and sustainable business value creation.
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
Upon finishing the training, you will be able to:
1
Build Machine Learning, Deep Learning, and predictive analytics solutions using Python, TensorFlow, and Keras
2
Develop NLP applications, conversational AI systems, and LLM-powered assistants for real-world business use cases
3
Create Generative AI and Multimodal AI solutions using Transformers, GANs, Diffusion Models, and Prompt Engineering techniques
4
Design and implement Retrieval-Augmented Generation (RAG) applications using LangChain and modern AI frameworks
5
Build autonomous AI agents and multi-agent workflows using CrewAI, LangGraph, and Agentic AI principles
6
Deploy enterprise-ready AI solutions with FastAPI, Power BI, AI governance practices, and hands-on industry simulation projects
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
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Frequently asked questions
The Artificial Intelligence and Applied Gen AI Certification in Dubai is a 90-hour training program that develops skills in Python, Machine Learning, Deep Learning, NLP, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.
Through hands-on projects using TensorFlow, LangChain, CrewAI, LangGraph, FastAPI, and Power BI, participants learn to build, deploy, and manage modern AI solutions for real-world business applications and high-demand AI careers.
The Artificial Intelligence and Applied Gen AI Training in Dubai prepares participants for a wide range of careers in Artificial Intelligence, Generative AI, LLM Engineering, AI Automation, and Enterprise AI Development. These career paths include:
- AI Engineer – Build and deploy AI-powered applications
- Generative AI Engineer – Develop advanced GenAI solutions
- LLM Engineer – Create and optimise LLM-based applications
- Machine Learning Engineer – Design predictive AI models
- Data Scientist – Extract insights from complex datasets
- AI Solutions Architect – Lead enterprise AI implementation
- AI Automation Specialist – Develop intelligent automated workflows
This certification course stands out from other courses offered in Dubai by combining Machine Learning, Deep Learning, Generative AI, LLMs, RAG, and Agentic AI within a single structured learning pathway. Participants gain hands-on experience using LangChain, CrewAI, LangGraph, FastAPI, and Power BI.
They also work on practical projects and the Industry Simulation that mirrors real business challenges. This course also offers flexible learning options, making advanced AI training accessible to working professionals and aspiring AI specialists.
Throughout the training, participants gain hands-on experience with Python, Jupyter Notebook, NumPy, Pandas, TensorFlow, Keras, Power BI, and FastAPI for AI development, analytics, and deployment.
They also work with LLMs, LangChain, RAG, Hugging Face Transformers, vector databases, CrewAI, and LangGraph to build intelligent applications.
These technologies are applied through practical projects involving Generative AI, AI assistants, autonomous agents, multi-agent workflows, and enterprise AI solutions.
No, prior coding experience is not required. This training begins with Python fundamentals, data analysis, statistics, and programming basics before progressing to Machine Learning, Deep Learning, Generative AI, LLMs, RAG, and Agentic AI concepts.
The structured learning pathway helps beginners build confidence through guided exercises, practical projects, and real-world applications. While familiarity with logic or data analysis can be helpful, the course is designed to support individuals from both technical and non-technical backgrounds.
The Industry Simulation in this Gen AI course provides hands-on experience in applying AI, Generative AI, LLMs, RAG, and Agentic AI to realistic business scenarios. Participants work on practical challenges and design AI-driven solutions for real-world organisational needs. The simulation enables participants to build:
- AI assistants for business productivity
- RAG-powered applications for knowledge retrieval
- Agentic AI workflows and automation solutions
- Predictive analytics and decision-support systems
- Enterprise AI deployment use cases
- Cross-functional business process optimisation
- AI governance and responsible AI implementation practices
With dozens of AI certification programs available today in the UAE, the difference lies in depth, delivery, and real-world impact. Learners Point is built for professionals who want more than just a credential.
Here is how Learners Point compares to other institutes:
- End-to-end curriculum depth: While most institutes cover either foundational AI or Generative AI, Learners Point delivers both in a unified certification spanning 35 modules.
- Real-world application over theory: Through dedicated Industry Simulations and case studies that most competing programs simply do not offer.
- Practitioner-led instruction: From faculty with active industry experience in AI, Machine Learning, and Generative AI, we ensure the curriculum stays aligned with what employers are actually hiring for right now.
- A broader technology stack: We cover not just LLMs but also LangChain, CrewAI, LangGraph, RAG pipelines, vector databases, multimodal AI, and agentic frameworks.
- Career-focused outcomes: With structured mentorship, portfolio-building projects, and certification that is recognised by employers across industries.
- A learner-first experience: It is designed specifically for working professionals, with flexible scheduling, personalised support, and a collaborative environment.
For mid-level IT professionals, this certification builds practical expertise in Artificial Intelligence, Generative AI, LLMs, RAG, and Agentic AI, helping accelerate career growth in the UAE's evolving technology landscape. This course helps to:
- Strengthen your profile with AI and Generative AI expertise
- Develop skills in LLMs, RAG, and AI Automation
- Open opportunities in AI Engineering and AI Solutions Architecture
- Support transition into enterprise AI and intelligent automation roles
- Prepare you for AI transformation initiatives
- Enhance your value in the UAE's digital economy
- Build experience through hands-on projects and Industry Simulation
Yes, you can attend the Artificial Intelligence and Applied Gen AI Course at GCC locations other than Dubai. To help you achieve this certification with ease, we offer our training across various GCC locations like:
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