Artificial Intelligence and Machine Learning Course in Qatar
Master Python, Pandas, NumPy, and Tableau tools in 90 hours
Learn ML, NLP, and Deep Learning techniques
Work on 5+ industry use cases and a capstone project
Access CareerHub for interviews and resumes
Gain lifetime access to classes and CareerHub for job readiness
4.9/56167 EnrolledEnrolled Learners
4.9/56167 EnrolledEnrolled Learners
Overview
What you will learn:
- Understanding AI, machine learning, deep learning, and generative AI foundations
- Applying prompt engineering and Claude tools for workplace productivity
- Creating predictive machine learning models with Python and statistics
- Constructing neural architectures and deep learning models for analysis
- Building NLP, Generative AI, and RAG-driven business solutions
- Developing agentic AI systems and autonomous multi-agent workflows
- Connecting AI models with Power BI dashboards and APIs
- Launching, tracking, and managing responsible, production-ready AI systems
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
After completing the training, you will be able to:
1
Build complete machine learning and deep learning systems for organizations
2
Create Generative AI, RAG, and LLM solutions for business use
3
Develop autonomous agentic AI workflows and multi-agent orchestration systems
4
Embed predictive algorithms and AI insights into Power BI dashboards
5
Learn to deploy and manage AI systems in operational settings
6
Use responsible AI principles on real-world business challenges
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
In the Artificial Intelligence and Machine Learning Course in Qatar, participants gain practical skills in Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, and Agentic AI. This curriculum covers prompt engineering, Python-based predictive modelling, neural networks, NLP, RAG systems, multi-agent workflows, and Power BI integration.
Participants also explore autonomous AI systems, AI deployment approaches, and responsible AI principles. Each module focuses on workplace applications, helping participants apply their learning to data-driven projects and AI-related initiatives in Qatar.
Yes. The training is designed to accommodate working professionals in Qatar with limited coding experience. This program begins with Artificial Intelligence foundations and prompt engineering before introducing Python essentials for data analysis in a structured, practical way.
Participants progress gradually through statistical concepts, Machine Learning models, and Generative AI applications. Each stage includes guided exercises so professionals can build confidence and apply skills without needing advanced prior programming knowledge.
Yes, participants can apply the skills from this course immediately in workplace projects in Qatar. The course curriculum focuses on practical techniques such as prompt engineering, building Machine Learning models with Python, creating RAG systems, and integrating insights into Power BI dashboards.
Every module includes business-focused exercises and real scenarios, so participants develop solutions that transfer directly to organizational tasks. This approach supports faster contribution towards data analysis, intelligent automation, and Generative AI initiatives without lengthy additional preparation.
Yes, this program includes dedicated coverage of prompt engineering and AI productivity tools such as Claude. Participants learn how to write effective prompts for business tasks, summarise documents, support research, and automate routine workflows using structured techniques.
The course also explores Claude alongside other AI assistants and demonstrates how role-specific AI workflows can be developed. These skills help participants improve workplace productivity and apply Generative AI tools confidently in everyday professional settings across Qatar.
Yes, this training includes a comprehensive industry simulation that reflects real enterprise challenges in Qatar and the wider region. Participants take on the role of an Enterprise Artificial Intelligence Center of Excellence and lead a large-scale transformation initiative across multiple business functions.
The simulation covers the full AI lifecycle, from identifying organizational problems and preparing data to building predictive models, Generative AI solutions, RAG architectures, and autonomous agent workflows. Participants also integrate insights with business intelligence tools and explore governance requirements within the simulated environment.
Assessment in this AI and ML training in Qatar focuses on practical competency rather than theoretical recall alone. Participants receive continuous guidance that tracks progress at every stage, with skills validated through case studies, scenarios, and workplace-style activities that reflect real organizational challenges.
Formative feedback is provided during the training to help participants refine their approach. A final summative assessment consolidates the learning covered during the program and evaluates overall performance against defined criteria. Participants who successfully complete the program receive a certificate confirming completion of the training.
Yes, teams from different functions in Qatar can benefit from this AI and ML program. The course curriculum is designed for professionals across HR, finance, analytics, project management, and operations who need practical skills in Artificial Intelligence, Machine Learning, and Generative AI.
Participants develop shared capabilities in data analysis, predictive modelling, intelligent automation and Power BI integration. This cross-functional approach helps teams collaborate more effectively on AI-driven projects and support organization-wide digital transformation initiatives without requiring every participant to have a technical background.
Learners Point has supported professional development across the Middle East since 2001, with a consistent focus on practical capability building for organizations and individuals in the GCC.
These are the reasons Learners Point stands out for this training:
- Over two decades of experience delivering professional training across the UAE, Saudi Arabia, and the wider MENA region
- Proprietary Friction Methodâ„¢ that links concepts directly to workplace application and measurable skill improvement
- Structured industry simulation where participants operate as an Enterprise AI Center of Excellence
- Practical assessment approach using case studies, scenarios, and continuous feedback rather than theory-only testing
- Curriculum covering the full pathway from prompt engineering and Python to Generative AI, RAG, and Agentic AI systems
- Strong emphasis on responsible AI and business intelligence integration for real organizational use
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