Artificial Intelligence and Applied Gen AI Certification
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
4.8/53245 EnrolledEnrolled Learners
4.8/53245 EnrolledEnrolled Learners
Overview
We will teach you to:
- Master Python, Machine Learning, Deep Learning, 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 solutions with Transformers, GANs, Diffusion Models, and Multimodal AI frameworks
- Design autonomous AI agents, multi-agent workflows, and Agentic AI systems using CrewAI and LangGraph
- Deploy AI applications with FastAPI, integrate predictive models with Power BI, and build business intelligence solutions
- Apply Responsible AI, governance, monitoring, and deployment practices for enterprise environments
- Progress through AI Foundations → Data Science → Machine Learning → Deep Learning → LLM Engineering → Agentic AI & Autonomous 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
Upon finishing the training, you will:
1
Build Machine Learning, Deep Learning, and predictive analytics models using Python, TensorFlow, and Keras
2
Develop NLP applications, conversational AI solutions, and LLM-powered assistants for business use cases
3
Create Generative AI applications using Transformers, GANs, Diffusion Models, and Multimodal AI frameworks
4
Design and implement Retrieval-Augmented Generation (RAG) solutions using LangChain and vector databases
5
Build autonomous AI agents, multi-agent workflows, and Agentic AI systems using CrewAI and LangGraph
6
Deploy AI applications through FastAPI, APIs, and business intelligence platforms such as Power BI
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
Related courses
Frequently asked questions
The Artificial Intelligence and Applied Gen AI Certification is a comprehensive training program that equips participants with practical skills in Python, data science, machine learning, deep learning, natural language processing (NLP), Generative AI, and Large Language Models (LLMs). The course also covers Retrieval-Augmented Generation (RAG), multimodal AI, prompt engineering, Agentic AI, and autonomous AI systems, enabling participants to build modern AI-powered applications for real-world business scenarios.
Participants gain hands-on experience in industry-relevant tools and frameworks such as Python, TensorFlow, Keras, LangChain, CrewAI, LangGraph, FastAPI, and Power BI. Through practical projects and enterprise-focused simulations, they learn to develop, deploy, govern, and optimise AI solutions, preparing them for roles in AI engineering, machine learning, Generative AI, intelligent automation, and enterprise AI transformation.
The Artificial Intelligence and Applied Gen AI course covers a comprehensive range of topics, including Python, data science, machine learning, deep learning, natural language processing (NLP), Generative AI, predictive analytics, business intelligence, and AI deployment. Participants also learn advanced concepts such as:
- Prompt Engineering
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- LangChain
- Multimodal AI
- Agentic AI
- CrewAI
- LangGraph
- Multi-Agent Systems
- Responsible AI Governance
The course combines theory with practical application, enabling participants to build predictive models, AI assistants, intelligent chatbots, RAG-powered knowledge systems, autonomous AI agents, and enterprise AI solutions. Through hands-on projects and industry-focused simulations, participants gain experience in developing, deploying, and managing modern AI applications for real-world business environments.
Earning this AI certification can accelerate career growth by building practical expertise in Python, Machine Learning, Deep Learning, Natural Language Processing (NLP), Generative AI, and modern AI development frameworks. Participants also gain hands-on experience in building Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) systems, Agentic AI workflows, autonomous AI agents, and enterprise AI solutions, helping them address real business challenges with confidence.
This certification ensures proficiency in high-demand skills like LLM Engineering, AI Automation, Multi-Agent Systems, LangChain, CrewAI, LangGraph, AI Deployment, and Responsible AI Governance. Through practical projects, enterprise AI simulations, and real-world implementation exercises, participants develop job-ready capabilities for roles such as AI Engineer, Generative AI Engineer, Data Scientist, and AI Automation Specialist.
Yes, this course is suitable for beginners. It starts with Python programming, data science, statistics, and data analysis fundamentals before progressing to Machine Learning, Deep Learning, NLP, and Generative AI concepts.
Participants gradually develop practical skills through guided exercises and projects, eventually learning to build LLM-powered applications, RAG systems, Agentic AI workflows, and autonomous AI solutions without requiring prior AI experience.
This training program is ideal for aspiring AI professionals, data analysts, data scientists, software developers, IT professionals, business analysts, and technology leaders seeking practical AI expertise.
It is also suitable for individuals interested in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and AI Automation. This course also benefits fresh graduates and professionals transitioning into AI, data science, and enterprise AI roles.
Choosing the right institute for an AI certification is as important as choosing the course itself. Learners Point has built a proven reputation for delivering industry-aligned and career-transforming training that goes far beyond the classroom. The reasons are as follows:
- Industry-experienced faculty and mentors who bring real-world AI and data science expertise into every session, ensuring participants receive guidance that is directly applicable to the job market.
- A future-ready curriculum that covers the full AI spectrum from Python and Machine Learning fundamentals to LLMs, Generative AI, RAG, Agentic frameworks, and Autonomous AI systems.
- Hands-on and project-based learning through Industry Simulations and case studies that equip participants with a job-ready portfolio.
- A strong record of career outcomes with a track record of helping professionals transition into high-demand AI roles across industries, including finance, healthcare, retail, and technology.
- Flexible and learner-centric delivery designed to accommodate working professionals with structured support, personalised mentorship, and a collaborative learning environment.
No, prior coding experience is not required before enrolling in this training. This program begins with Python programming, data science fundamentals, statistics, and data analysis before progressing to Machine Learning, Deep Learning, Generative AI, LLMs, RAG, and Agentic AI concepts.
This structured learning pathway helps beginners build confidence and develop practical AI skills step by step, even without a technical background.
Participants gain hands-on experience with industry-relevant AI, Machine Learning, Generative AI, and Agentic AI tools used to develop, deploy, and manage modern AI solutions. These include the following tools:
- Python, Jupyter, NumPy & Pandas – for data analysis and AI development
- TensorFlow & Keras – for Machine Learning and Deep Learning models
- LangChain – for building RAG applications and AI workflows
- CrewAI & LangGraph – for creating Agentic AI and multi-agent systems
- Hugging Face Transformers – for LLMs, NLP, and model fine-tuning
- FastAPI – for deploying AI-powered applications and APIs
- Power BI – for AI-driven analytics and business intelligence solutions
AI is integrated throughout the training through hands-on projects, practical exercises, and real-world business applications. Participants use LLMs, Prompt Engineering, LangChain, RAG, CrewAI, LangGraph, and Agentic AI frameworks to build intelligent solutions across multiple modules.
This approach helps them apply AI to automation, decision-making, multi-agent workflows, AI deployment, and responsible AI governance, ensuring practical and workplace-ready skills.
Upon successful completion of the program, participants receive the Artificial Intelligence and Applied Gen AI Certification from Learners Point, approved by KHDA (Knowledge and Human Development Authority – Dubai).
This certification validates practical expertise in Machine Learning, Deep Learning, Generative AI, LLMs, Retrieval-Augmented Generation (RAG), Agentic AI, and AI deployment. It shows industry-relevant capabilities in developing, implementing, and managing modern AI solutions.
Yes, this Artificial Intelligence and Applied Gen AI course will be available for GCC locations. We provide our training across various GCC regions, which helps you to achieve this credential easily. These regions are:
Do you want to learn more about Learners Point Academy?
- Learn more about courses
- Understand about our methodology
- Let’s talk about Corporate trainings
- Anything else that you want to know, we are here for you!
