Artificial Intelligence and Applied Gen AI Certification in Saudi Arabia
90 hours of AI, GenAI & Agentic Learning training program
Globally recognised AI certification with LLMs & RAG Systems
Learn LangChain, CrewAI & LangGraph applications
35 modules, enterprise AI simulation & real-world deployment
Flexible learning options with easy instalments
4.8/53645 EnrolledEnrolled Learners
4.8/53645 EnrolledEnrolled Learners
Overview
What you will master with us:
- Master Python, Machine Learning, Deep Learning, LLMs, and Generative AI through a structured 90-hour training program
- Build NLP applications, AI assistants, chatbots, and enterprise-ready LLM solutions
- Develop Retrieval-Augmented Generation (RAG) applications using LangChain and modern AI frameworks
- Create Generative AI and Multimodal AI projects using Transformers, GANs, Diffusion Models, and Prompt Engineering
- Design autonomous AI agents and multi-agent workflows using CrewAI, LangGraph, and Agentic AI frameworks
- Apply AI to business scenarios using Power BI, FastAPI, predictive analytics, and deployment frameworks
- Prepares professionals for production-ready roles in AI, Generative AI, and AI automation
- Consists of a structured pathway: Foundation → Data Science → Machine Learning → Deep Learning → Generative AI → LLM Engineering → Agentic AI
Upcoming sessions
Curriculum
1
What is AI, ML, and GenAI
2
AI vs Human Intelligence
3
History and Evolution of AI
4
Current AI Tools Landscape
5
Real-World Business Applications
6
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
1
Introduction to Claude.ai Interface
2
Prompt Engineering Fundamentals
3
Writing Effective Prompts for Business
4
Document Summarization & Drafting
5
Claude for Research and Analysis
6
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
1
Python Syntax, Variables, Data Types
2
Conditional Statements and Loops
3
Functions and Lambda Functions
4
Lists, Tuples, Dictionaries, Sets
5
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
1
Jupyter/Colab Usage
2
NumPy Arrays and Indexing
3
Vectorized Operations
4
Matrix Transformations
5
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
1
Data Loading and Cleaning
2
Handling Missing Values
3
Data Merging
4
Data Transformation
5
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
1
Statistical Measures
2
Mean, Median, Mode
3
Variance and Standard Deviation
4
Distribution Analysis
5
Probability Rules
6
Normal Distribution
7
Binomial Distribution
8
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
1
Central Limit Theorem
2
Confidence Intervals
3
t-Tests
4
Chi-Square Tests
5
ANOVA
6
A/B Testing
7
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
1
Histograms and Bar Charts
2
Boxplots and Heatmaps
3
Correlation Analysis
4
Visualization Best Practices
5
Dashboard-Ready Charts
AI Integration
- Recommend effective visualization approaches
- Enhance storytelling through AI-generated insights
Activities/Case Study
Retail Performance Visualization and Trend Analysis
1
Feature Engineering
2
Scaling and Encoding
3
Outlier Detection
4
Data Exploration Workflows
5
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
1
Machine Learning Types and Workflows
2
Model Training and Evaluation
3
Performance Metrics
4
Model Validation
5
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
1
Linear Regression
2
Logistic Regression
3
Model Interpretation
4
ROC-AUC
5
Confusion Matrix
6
Prediction Systems
AI Integration
- Evaluate predictive model effectiveness
- Generate prediction-focused business insights
Activities/Case Study
Sales Forecasting and Revenue Prediction
1
Decision trees
2
Random forests
3
KNN algorithm
4
Model tuning
5
Predictive modeling use cases
AI Integration
- Compare classification model performance
- Improve predictive decision-making capability
Activities/Case Study
Customer Risk Assessment and Classification Analysis
1
K-means clustering
2
Hierarchical clustering
3
PCA
4
Feature reduction
5
Segmentation models
AI Integration
- Generate customer segmentation insights
- Support pattern discovery and clustering analysis
Activities/Case Study
Market Segmentation and Customer Profiling Analysis
1
Data Integration
2
Power Query Transformations
3
Visual Dashboards
4
KPI Tracking
5
Reporting Workflows
6
Data Modeling
7
DAX Calculations
8
Multi-Page Dashboards
9
Forecasting Models
10
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
1
Model Export and Integration
2
Dashboard Automation
3
Predictive Analytics Visualization
4
Business Intelligence Workflows
5
Data Storytelling
AI Integration
- Integrate predictive models into dashboards
- Generate actionable business recommendations
Activities/Case Study
Telecom Churn Prediction and Reporting Solution
1
Artificial Neural Network Concepts
2
Activation Functions
3
Forward Propagation
4
Backward Propagation
5
Keras Implementation
6
Neural Network Design
AI Integration
- Design neural network architectures efficiently
- Optimize model structure and performance
Activities/Case Study
Customer Churn Prediction Using ANN Models
1
TensorFlow and Keras
2
Loss Functions and Optimizers
3
Overfitting and Underfitting
4
Model Tuning
5
Training Workflows
AI Integration
- Improve training and tuning workflows
- Optimize neural network performance
Activities/Case Study
Sales Forecasting Using Deep Learning Models
1
Text preprocessing
2
Tokenization and lemmatization
3
Vectorization techniques
4
NLP workflows
5
Sentiment analysis
AI Integration
- Automate text analysis workflows
- Extract insights from unstructured data
Activities/Case Study
Customer Feedback Analysis Using NLP Techniques
1
Logistic regression and Naive Bayes
2
Word embeddings
3
Classification models
4
Text categorization
5
Model evaluation
AI Integration
- Improve text classification accuracy
- Generate NLP-driven business insights
Activities/Case Study
Email Categorization and Support Ticket Routing
1
Image processing
2
CNN architecture
3
Transfer learning
4
Model storage
5
Deployment basics
AI Integration
- Build image recognition solutions
- Accelerate computer vision implementations
Activities/Case Study
Product Defect Detection Using Computer Vision
1
Flask and FastAPI
2
REST API development
3
Model deployment
4
Integration workflows
5
Production considerations
AI Integration
- Deploy AI models into production systems
- Streamline API-based AI integration
Activities/Case Study
AI-Powered Recommendation Service Deployment
1
Reinforcement Learning Concepts
2
Q-Learning
3
Agent-Environment Interaction
4
Exploration vs Exploitation
5
Simulation Models
AI Integration
- Develop intelligent decision agents
- Optimize learning and simulation workflows
Activities/Case Study
Warehouse Optimization Through Reinforcement Learning
1
Self-Attention Mechanisms
2
Transformer Architecture
3
BERT vs GPT
4
Positional Encoding
5
Text Generation
AI Integration
- Build transformer-based AI solutions
- Improve language model understanding
Activities/Case Study
Enterprise Document Summarization Using Transformer Architecture
1
GAN Architecture
2
Generator and Discriminator
3
GAN Variants
4
Image Generation
5
Model Training
AI Integration
- Create AI-generated visual content
- Improve generative model performance
Activities/Case Study
Marketing Content Creation Using GAN-Generated Visuals
1
Diffusion Process
2
DDPM
3
Stable Diffusion Tools
4
Image Generation Workflows
5
Model Comparison
AI Integration
- Generate high-quality AI-created media
- Compare generative model approaches
Activities/Case Study
Creative Asset Production Using Diffusion Models
1
GPT and LLM concepts
2
Prompt engineering
3
Fine-tuning techniques
4
Domain-specific models
5
Chatbot development
AI Integration
- Develop domain-specific AI assistants
- Optimize prompts and model performance
Activities/Case Study
Customer Support Chatbot Development Using GPT Models
1
LangChain Architecture
2
Vector Databases
3
Retrieval-Augmented Generation
4
Knowledge Integration
5
AI Pipelines
AI Integration
- Build enterprise RAG solutions
- Connect LLMs with organizational knowledge
Activities/Case Study
Enterprise Policy and Document Retrieval Solution
1
Vision-Language Models
2
Media Generation
3
Multimodal Systems
4
AI Creativity Tools
5
Application Design
AI Integration
- Develop multimodal AI experiences
- Integrate text, image, and media intelligence
Activities/Case Study
AI-Powered Multimedia Marketing Solution
1
Multi-Agent Systems
2
Autonomous Workflows
3
Decision Agents
4
Task Orchestration
5
AI Assistants
AI Integration
- Design autonomous AI workflows
- Build intelligent decision-making agents
Activities/Case Study
Automated Customer Support Orchestration System
1
Multi-Agent Collaboration
2
LangChain Dynamic Decision Agents
3
Autonomous Assistant Architecture
4
Agent Communication
5
Collaborative Workflows
AI Integration
- Enable agent collaboration and coordination
- Develop adaptive AI assistant workflows
Activities/Case Study
Cross-Functional Business Process Automation Using Agents
1
Task Decomposition and Planning
2
Reflex vs Learning Agents
3
CrewAI Orchestration
4
LangGraph Fundamentals
5
Multi-Agent Workflow Design
6
Agent Collaboration Strategies
AI Integration
- Orchestrate complex AI task execution
- Build scalable multi-agent workflows
Activities/Case Study
Autonomous Project Management and Task Coordination
1
LangChain Tools
2
Search Tool Integration
3
API Connectivity
4
Zapier Integrations
5
Agent Memory Architectures
6
Vector Memory
7
Long-Term Memory
8
Summary Memory
9
Context Retention Strategies
10
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
1
Function Calling Fundamentals
2
ReAct Prompting Framework
3
Toolformer Concepts
4
Tool-Based Reasoning
5
Dynamic Tool Selection
6
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
1
Prompt → Tools → Output Orchestration
2
Agent Workflow Management
3
Guardrails and Safety Controls
4
Observability and Monitoring
5
Evaluation Metrics
6
Production Workflows
7
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
1
AI Ethics and Bias
2
Responsible AI Principles
3
Model Governance
4
AI Risk Management
5
Deployment Platforms
6
Monitoring and Evaluation
7
Compliance Requirements
8
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
1
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.
2
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.
3
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.
4
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.
5
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:
1
Build Machine Learning, Deep Learning, and predictive analytics solutions using Python, TensorFlow, and Keras
2
Develop NLP applications, AI assistants, and LLM-powered solutions for real-world business scenarios
3
Create Generative AI and Multimodal AI applications 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 using 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.
Overall ratings by our students
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Frequently asked questions
The Artificial Intelligence and Applied Gen AI Certification in Saudi Arabia covers Machine Learning, Deep Learning, NLP, Generative AI, LLMs, RAG, and Agentic AI. Participants gain hands-on experience with Python, TensorFlow, LangChain, FastAPI, CrewAI, and Power BI as they build practical AI solutions.
This training program prepares professionals to develop, deploy, and manage enterprise AI applications, AI assistants, and intelligent automation workflows.
This certification is designed for individuals who want to build expertise in Artificial Intelligence, Generative AI, LLMs, and AI Automation. This program supports both technical and non-technical professionals seeking AI-focused career growth. Some of these candidates are:
- IT Professionals and Software Engineers
- Data Analysts and Data Scientists
- AI and Machine Learning Aspirants
- Business Leaders and Digital Transformation Managers
- Automation and Technology Professionals
- Students and Fresh Graduates
- Researchers and Innovation Specialists
Earning this certification helps professionals build expertise in Artificial Intelligence, Generative AI, LLMs, RAG, and Agentic AI while preparing for high-growth opportunities in the KSA's digital economy. The benefits are:
- Develop in-demand AI and Generative AI skills
- Build expertise in LLMs, RAG, and AI Automation
- Gain experience through real-world projects and Industry Simulation
- Prepare for AI Engineering and AI Solutions Architect roles
- Support digital transformation and enterprise AI initiatives
- Earn a globally recognised certification
- Enhance long-term career growth and earning potential
No. Prior programming experience is not required to join this AI certification course in Riyadh. This course starts with Python fundamentals, data analysis, statistics, and AI foundations, then progresses to Machine Learning, Deep Learning, Generative AI, LLMs, RAG, and Agentic AI.
Through guided exercises, practical projects, and hands-on learning, participants gradually build the skills needed to develop and deploy modern AI solutions, regardless of their technical background.
Yes, the certification training in the KSA concludes with the Industry Simulation which enables participants to apply their knowledge in a realistic business environment. Learners work on end-to-end projects involving Machine Learning, Generative AI, LLMs, RAG, AI Assistants, and Agentic AI workflows.
The simulation helps participants design, build, and deploy practical AI solutions, showing workplace-ready skills while strengthening their professional portfolio and confidence in real-world AI implementation.
As Saudi Arabia accelerates its Vision 2030 digital transformation agenda, the demand for skilled AI professionals has never been greater. Learners Point is uniquely positioned to help you meet that demand. The reasons why you should choose Learners Point:
- Locally relevant and globally recognised training designed to align with the KSA's growing AI and digital economy ambitions.
- A curriculum built for the future of work in Saudi Arabia covering Machine Learning, Generative AI, LLMs, and Agentic AI systems.
- Experienced and practitioner-led instruction from industry professionals who understand the regional business landscape.
- The unique Industry simulation that allows you to apply AI knowledge in a realistic business environment.
- Flexible learning options suited to working professionals across KSA, that are structured to fit around demanding schedules while maintaining the depth, rigour, and mentorship support.
- A trusted training partner with a strong track record of upskilling professionals across the Middle East, making it one of the most credible and reliable choices for AI certification training in Saudi Arabia.
In the Artificial Intelligence and Applied Gen AI Certification in Riyadh, you gain practical expertise with industry-standard AI tools. Our training ensures you are hands-on with platforms and frameworks widely used in real-world AI projects. The tools are:
- Python & Jupyter Notebook – AI development and data analysis
- TensorFlow & Keras – Machine Learning and Deep Learning
- LangChain & FastAPI – LLM application development
- Hugging Face Transformers – Generative AI and NLP projects
- CrewAI & LangGraph – Agentic AI and multi-agent workflows
- Power BI – AI-driven analytics and visualisation
- Vector Databases & RAG Tools – Knowledge retrieval application
Yes, this AI course in Saudi Arabia includes in-depth learning on Generative Adversarial Networks (GANs). You will explore how GANs work with a generator and discriminator to create realistic synthetic data, images, and other outputs that mimic real-world patterns.
Our course covers popular GAN architectures such as DCGAN and CycleGAN. We teach you how to apply them in fields like image synthesis, data augmentation, and creative AI applications, which boosts your advanced AI expertise.
Yes, you can enrol in the Artificial Intelligence and Applied Gen AI Course from locations outside Saudi Arabia. We deliver our Applied AI training across various locations, which helps you achieve this credential. These regions are:
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