• Empowers professionals to lead in the rapidly evolving world of gen ai and Agentic Systems.
• Offers immersive, project-based learning—no passive lectures, only real creation.
• Keeps pace with the latest in generativeai and AI agents to meet industry demand.

• Hands-on mastery of LLMs, GANs, diffusion models, and autonomous agents.
• Build real-world projects: chatbots, AI art, smart assistants, and more.
• Learn using top-tier tools: OpenAI API, Stable Diffusion, LangChain, and Hugging Face.
• Gain skills in LLM fine-tuning, reinforcement learning, and responsible AI practices.
• Deploy models effectively on cloud and edge platforms.
• Earn a career-recognized certificate and enjoy lifetime course access.

• AI/ML Foundations Refresher 
  • Deep Learning Overview
  • Convolutional Neural Networks (CNNs) & Recurrent Neural Networks (RNNs)
  • Transformers & Attention Mechanism
  • Autoencoders & Variational Autoencoders (VAEs)
  • Evaluation Metrics for Generative Models

• Core Generative AI Topics
  • Generative Adversarial Networks (GANs)
  • GAN Architecture
  • Advanced GAN Architectures
  • Hands-on Project
  • Diffusion Process
  • Popular Diffusion Models
  • Hands-on Project
  • Large Language Models (LLMs) & Transformers
  • NLP Techniques
  • LangChain and RAG Architecture
  • Hands-on Project
  • Multimodal AI – s
  • Vision-Language Models
  • Applications in Content Creation
  • Text-to-Image, Text-to-Video, and Text-to-Audio generation (, OpenAI Sora, DALL·E 2)
  • Benefits and challenges of multimodal AI
  • Hands-on Project

• Hands-on Projects & Advanced Concepts 
  • Fine-tuning an LLM on Domain-Specific Data
  • Building Autonomous Chatbots with Retrieval-Augmented Generation (RAG)
  • AI-Generated Media Projects
     
• Agentic AI: Autonomous Decision-Making 
  • Introduction to Agentic AI & Autonomous Systems
  • Reinforcement Learning for Autonomous Decision-Making
  • Multi-Agent Systems & Collaboration
  • LangChain for Autonomous Agents

• Responsible AI & Deployment Best Practices
  • Ethical AI
  • Deployment Techniques

• Deep expertise: training led by AI industry professionals with over a decade of experience.
• State-of-the-art resources: access to cutting-edge tools and real-world case studies.
• Ongoing support: lifetime access, career guidance, and flexible payment plans.
• Proven credibility: highly rated and trusted by thousands of learners

Cost: 3500 AED

Duration: 40 Hours

• Growing Demand in the EV Industry: The electric vehicle sector is expanding rapidly, particularly in regions like UAE and Dubai, creating a strong demand for skilled professionals.
• Industry-Relevant Skills: This course equips you with the practical skills and knowledge needed to excel in EV engineering and system integration, ensuring you are well-prepared for the workforce.
• Real-World Application: The course offers hands-on experience with industry-standard tools like MATLAB, Simulink, Ansys, and Python, making you job-ready for advanced roles in the EV industry.
• Comprehensive Coverage: From EV architecture to powertrain systems, battery management, and thermal strategies, this course covers all crucial aspects of EV technology.
• Career-Ready Preparation: The course includes job roles and responsibilities specific to the EV industry, giving you a clear path to success in an expanding market.
• Sustainable Future: Gain insights into sustainability and innovations in EV manufacturing and circular economy, making you an asset to companies prioritizing green technologies.

• Industry-Focused Learning: Gain practical experience working with tools like MATLAB, Simulink, and Ansys, ensuring a deep understanding of EV systems and components.
• Hands-On Projects: Develop 5+ real-world industry use cases across diverse EV scenarios.
• Expert Instructors: Learn from industry experts with over a decade of experience in the EV and tech sectors.
• Job Market Preparedness: Familiarity with job roles and responsibilities in the EV industry, preparing you for immediate career opportunities.
• Lifetime Access: Enjoy lifetime access to course materials and future updates, ensuring your skills remain current.

• EV vs. Internal Combustion Engine (ICE) Overview
• EV Types: BEV, HEV, PHEV, FCEV – Pros & Cons
• Vehicle Development Process (Concept → Design → Testing → Deployment)
• Systems Integration Overview
• Case Study: Tesla vs. Traditional Automakers – EV Design Evolution

• Hybrid Components & Architectures
• Major Powertrain Components (Motors, Batteries, Power Electronics)
• Controls Integration & Component Sizing Trade-offs
• System Design Considerations (Energy, Efficiency, Torque, Thermal)
• Software Lab: MATLAB/Simulink – EV Powertrain Simulation

• Vehicle-Level Integration (Performance, Drivability, NVH)
• Chassis & Powertrain Interaction (Braking, Steering, Ride Handling)
• HV/LV Electrical Systems – Safety & Charging Architecture
• In-Vehicle Displays & Information Systems (User Experience & Efficiency Aids)
• Software Lab: CAN Communication & Embedded Control for EVs

• Testing & Validation Processes (Component, System, Fleet Testing)
• Regulatory Standards: SAE, UL, IEC, FMVSS
• Safety Testing & Certification (Short Circuit, Overcharge, Fire Exposure)
• Hands-on Demo: Battery Testing & EV Component Testing

• Maxwell’s Equations & Magnetic Circuits
• Motor Types: PM Motors, Induction Motors, Switched Reluctance Motors
• Torque Production, Non-linear Magnetic Behavior, Losses & Efficiency
• Hands-on Lab: Motor Control using MATLAB/Simulink

• Lithium-Ion Battery Chemistry, Design & Safety Considerations
• Battery Pack Design (Thermal, Electrical, Structural Considerations)
• Battery Management Systems (BMS) – Functions, Control, & Estimation
• Simulation Lab: Battery Pack Simulation using Simulink & Ansys

• Power Converters: Buck, Boost, Inverter Design & Efficiency
• Semiconductor Devices: IGBTs, MOSFETs, SiC/GaN Trends
• Charging Infrastructure & Standards: SAE J1772, Inductive & DC Fast Charging
• Lab: Converter & Inverter Control Design in MATLAB/Python

• Embedded Controllers & Communication (CAN, LIN, FlexRay)
• AI in EVs: Predictive Maintenance, Battery Health Monitoring
• Real-time BMS & Motor Control using AI/ML Models
• Hands-on: Implementing AI-Based Battery Management using Python

• Battery & Power Electronics Thermal Considerations
• Active vs. Passive Cooling – Air, Liquid, PCM, Heat Pipes
• Case Study: Tesla vs. Lucid vs. Rivian – Thermal Strategies
• Simulation Lab: Battery Thermal Management in ANSYS

• Manufacturing Processes: Cell-to-Pack & Cell-to-Chassis Innovations
• Sustainability & Second-Life Batteries (Reuse, Recycling, Disposal)
• Industry Panel: Experts on EV Manufacturing & Circular Economy

• Energy Management Strategies for Extended Range
• Vehicle-to-Grid (V2G) & Bi-directional Charging
• Hands-on: Developing Smart Charging Algorithms in Python

Cost: 3000 AED

Discount: 50% Off for Laimoon Users!

Duration: 3 Months

• Comprehensive Curriculum: Gain practical skills with 15 weeks of intensive industry-led training.
• Hands-On Experience: Work on 5+ industry projects and a capstone project to build real-world expertise.
• Essential Tools Covered: Master Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
• Career Support: Access CareerHub for resume building, job support, and interview preparation.
• Flexible Learning: Enjoy lifetime access to classes and easy payment options

• Develop expertise in machine learning models and data analysis.
• Master NLP and Deep Learning techniques for advanced data insights.
• Learn to use CNN and RNN models for complex problem-solving.
• Build confidence with industry-standard projects and a capstone project.

• Overview of Python: history, features, and use cases
• Installing Python and setting up the development environment
• Basic syntax: variables, data types, and simple operations.
• Hands-on: Writing a simple Python program to understand basic concepts

• Introduction to sequences: lists, tuples, sets, and dictionaries.
• File handling: reading and writing files in Python.
• Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events.
• Hands-on: A program simulating coin tosses or dice rolls to explore simple probability

• Defining and using functions: arguments, return values, and scope.
• Object-oriented concepts: classes, objects, inheritance, and polymorphism.
• Hands-on: Writing classes to represent basic statistical concepts (, creating a class for a random variable or distributions)

• Importing and using Python modules.
• Exception handling: try, except, and finally blocks.
• Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance)

• Introduction to NumPy and its applications.
• Creating and manipulating arrays.
• Mathematical Concepts
• Probability distributions
• Hands-on

• Basic Pandas operations
• Cleaning and transforming data
• Mathematical Concepts
• Hands-on

• Merging, joining, and reshaping data.
• GroupBy, pivot tables, and handling time-series data.
• Mathematical Concepts
• Hands-on

• Basic visualization
• Mathematical Concepts
• Hands-on

• Creating subplots, and advanced customizations in Matplotlib.
• Statistical visualizations in Seaborn
• Mathematical Concepts
• Hands-on

• Introduction to web scraping
• Using Selenium for scraping dynamic websites.
• Hands-on

• Introduction to Machine Learning
• Mathematics for Machine Learning
• Introduction to ML Tools

• Regression
• Classification
• Feature Engineering

• Ensemble Methods
• Hyperparameter Tuning
• Unsupervised Learning

• Time Series Analysis
• Big Data Tools
• Model Scalability

• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)
• Advanced Neural Network Architectures
• NLP Applications

• Ethics in AI
• Real-World Applications
• Capstone Projects

• Module: Data Preparation using Tableau Prep
• Module: Data Connection with Tableau Desktop
• Module: Basic Visual Analytics
• Module: Calculations in Tableau

• Module: Advanced Visual Analytics
• Module: Level of Detail (LOD) Expressions in Tableau
• Module: Advanced Charts in Tableau
• Module: Dashboards and Stories

• Expert Guidance: Learn from seasoned trainers who blend theory with practical insights.
• Interactive Learning: Participate in engaging sessions, hands-on projects, and collaborative activities.
• Job Assistance: Our CareerHub ensures you’re ready for the job market with the right tools and resources.
• Comprehensive Curriculum: Stay ahead of industry trends with the latest AI and ML techniques.

Cost: 3000 AED

Discount: 50% Off for Laimoon Users!

Duration: Upto 4 Months

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

• Popular Machine Learning Algorithms
• Clustering, Classification, and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm: Train, Evaluation, and Testing.
• How to choose Machine Learning Algorithm

• Simple Linear Regression: Theory, Implementing in Python (and R), Working on the use case
• Multiple Linear Regression: Theory, Implementing in Python (and R), Working on the use case
• K – Nearest Neighbors: Theory, Implementing in Python (and R), KNN advantages, Working on the use case
• Decision Trees: Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on the use case
• Random Forests: Theory, Implementing in Python (and R), Reliability of Random Forests, Working on the use case

• Naive Bayes Classifier: Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on the use case
• Support Vector Machines: Theory, Support vector machines with Python and R, Improving the performance with Kernels, Working on the use case
• Association Rules: Theory, Implementing in Python (and R), Working on the use case
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models

• K – Means Clustering: Theory, Euclidean Distance method.
• K – Means hands-on with Python (and R)
• K – Means Advantages & Disadvantages
• Hierarchical Clustering: Theory
• Hierarchical Clustering with Python (and R)
• Hierarchical Advantages & Disadvantages

• Dimensionality Reduction: Feature Extraction & Selection
• Principal Component Analysis (PCA): Theory, Eigen Vectors
• PCA example with Python (and R) with use case
• Advantages of Dimensionality Reduction
• Application of Dimensionality Reduction with a case study
• Collaborative Filtering & Its Challenges

• Become familiar with Machine Learning concepts
• Develop a deep understanding of the fundamentals of Machine Learning and Statistical Modeling
• Incorporate Machine Learning into practice
• Familiarize with Supervised and Unsupervised Learning
• Employ the concepts of Machine Learning at the workplace

• Our Machine Learning course is designed based on real-world scenarios, formulating and evaluating practical requirements
• Our courses strive to provide a reality check and insight into the requirements of a challenging role and how to fulfill it
• We also provide live interactive sessions and hands-on experience through assignments associated with the actual work environment
• To enhance the learning experience of our students we provide organized group sessions and integrated training modules
• Our discrete approach to career guidance enables our students to get successfully placed in renowned organizations

Cost: 2835 AED

Discount: 60% Off for Laimoon Users!

Duration: 35 Hours

• Introduction to Artificial Intelligence (AI)
• AI Data Strategy
• AI Ethics, Issues and Concerns
• AI Challenges
• Use Cases, and Adaption

• Data Science and ML Introduction
• Mathematics for ML
• Learning Methods
• Popular ML Algorithms
• Building Classification Models
• Building Regression Models
• Beyond Machine Learning

• Tensorflow Introduction
• Tensorflow Basic Concepts
• Installation and Basic Operations In TF
• TF Eager Mode
• Tensorflow – Keras

• Core Learning Algorithms Introduction
• Regression with Tensorflow
• Classification with Tensorflow

• Structure of Neural Networks
• Neural Network – Core Concepts
• Feed Forward Algorithm
• Backpropagation
• Building Neural Network from scratch using Numpy

• Introduction to Neural Networks with
• Simple Deep Learning Model in Keras ()
• Building Neural Network
• Model in Tf for Mnist Dataset

• Convolutional Neural Networks (CNNs) Introduction Cnnswith Keras
• Transfer Learning In CNN, Style Transfer
• Flowers Dataset With
• Examining X – Ray With CNN Model

• RNN Introduction
• Sequences with RNNs
• Long Short – Term
• Memory Networks (LSTM RNNs) and Gru
• Examples Of RNN Applications

• Natural Language Processing Introduction
• NLP With RNNs
• Creating Model
• Transformers and BERT
• State of art NLP and Projects

• Markov Decision Process
• Fundamental Equations in RL
• Model-Based Method
• Dynamic Programming Model Free Methods

• Architectures Of Deep Q Learning
• Deep Q Learning
• Policy Gradient Methods

• General Adversarial Network
• GAN Introduction
• Core Concepts Of GAN
• Building GAN Model With Tensorflow , GAN Applications
• Deploying Deep Learning Methods in the Cloud
• Amazon Web Services (AWS)
• Deploying Deep Learning Models Using AWS Sagemaker

• Understand the foundational concepts of Artificial Intelligence (AI)
• Integrate Machine Learning and Artificial Intelligence into the business
• Utilize the basic concepts of AI when implementing solutions that involve problem–solving, logic, inference, perception, knowledge representation, and learning
• Possess expertise in AI language expert systems or data mining tools
• Implement the scientific method for Machine Learning models

• Our Artificial Intelligence Expert course is designed based on real-world scenarios, formulating and evaluating practical requirements
• Our courses strive to provide a reality check and insight into the requirements of a challenging role and how to fulfill it
• We also provide live interactive sessions and hands-on experience through assignments associated with the actual work environment
• To enhance the learning experience of our students we provide organized group sessions and integrated training modules
• Our discrete approach to career guidance enables our students to get successfully placed in renowned organizations

Cost: 2835 AED

Discount: 60% Off for Laimoon Users!

Duration: 54 Hours