• 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

• 20 Hours of Expert Training: Led by industry expert Thavi.
• Certification: Earn a certificate from Learners Point Academy.
• Hands-On Learning: Real-world projects and case studies for practical experience.
• Comprehensive Curriculum: Explore AI-powered marketing tools and techniques.
• Flexible Payment Options: Easy instalment plans available.

• Introduction to AI in Digital Marketing: Understand AI’s role in modern marketing strategies.
• AI Technologies in Marketing: Explore tools like machine learning, NLP, and chatbots.
• Automating Marketing Tasks: Learn content optimization, email automation, and digital ad placement.
• User Behavior Analytics: Gain insights into customer journey analysis and audience segmentation.
• Customer Engagement with AI: Master chatbots, personalized content delivery, and recommendation engines.
• Real-World Case Studies: Analyze successful AI-powered campaigns and key learnings.
• Final Project: Create and present an AI-driven marketing strategy.

• Optimize Marketing Efforts: Use AI tools to automate repetitive tasks and improve efficiency.
• Enhance Customer Engagement: Deliver personalized content for higher audience retention.
• Boost Decision-Making: Gain insights through predictive analytics and user behavior analysis.

• Understanding AI & Machine Learning in Marketing
• Understand AI models in Marketing
• Types of Programming in AI
• How AI is Transforming Digital Marketing AI-Powered Analytics & Consumer Insights (Semrush, Google Analytics 4, Audience, etc.)
• Ethical Considerations & AI Compliance (GDPR, Data Privacy)

• AI approach in Content Marketing (Jasper, , Writesonic)
• AI-Powered SEO techniques (SurferSEO, Frase, MarketMuse)
• AI in Keyword Research, user queries & Topic research
• FAQs content for AI voice assistant optimisation
• AI-Powered Blog & Video Content Creation (Pictory, Synthesia)

• AI content technique for Social Media Marketing (Hootsuite, Sprout Social, )
• AI tools in Social Listening & Sentiment Analysis
• AI in Google Ads & PPC Automation ( Smart Bidding, goal optimisation, catalog ads, etc.)
• AI-Driven Personalization & Ad Targeting
• AI Chatbots & Conversational Marketing (ChatGPT, Drift, ManyChat)
• AI Prompt Engineering Techniques

• AI content in Email Marketing (Mailchimp AI, HubSpot, Klaviyo)
• AI-Powered Lead Scoring & Nurturing
• AI for Customer Experience (CX) Optimisation
• AI & Predictive Customer Behaviour Analysis

• Overview of AI Tools for Marketers
• Hands-on Experience with AI Tools & Platforms
• AI designing tools (Dall-E, Deepai, etc.)
• A/B test in content, campaign optimization & Analyses (AI v/s Manual)
• Future Trends in AI & Marketing

• Content Strategy with AI prompts
• Text & Visual content with AI
• Live Q&A & Case Studies Final Project Submission
• Certification

• Higher Employability: Mastering AI tools makes you highly desirable in the marketing field.
• Cutting-Edge Skills: Stay ahead by learning the latest AI-powered strategies.
• Leadership Opportunities: Prepare for roles as a Digital Marketing Specialist or AI Marketing Strategist.

Cost: 3000 AED

Discount: 60% Off for Laimoon Users!

Duration: 24 Hours

• 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

• Perception
• Reasoning
• Learning
• Decision making
• Natural language processing.

• History of Artificial Intelligence
• What is Artificial Intelligence?
• Application of Artificial Intelligence
• Artificial Intelligence Intelligent Systems
• Problems in Artificial Intelligence
• Approaches used in Artificial Intelligence
• Tools used in Artificial Intelligence
• Different types of Artificial Intelligence Technology
• Agents and Environments in Artificial Intelligence
• Artificial Intelligence Expert Systems
• Artificial Intelligence Robotics
• What is strong and weak Artificial Intelligence?

• Business executives and managers who want to understand the potential of AI and how it can be leveraged to gain a competitive advantage in their industry.
• IT professionals who want to learn the fundamentals of AI and how it can be integrated into their projects.
• Data scientists and analysts who want to expand their knowledge and skills in AI and its subfields.
• Anyone who is interested in learning about AI and its impact on society. The course is designed for individuals with little or no prior knowledge of AI, but it can also serve as a refresher for those who have some familiarity with the topic.

• What is AI?
• Brief history of AI
• Different types of AI and their applications
• Key concepts and terminology

• Introduction to machine learning
• Supervised  unsupervised  and reinforcement learning.
• Classification and regression
• Overfitting and underfitting
• Model evaluation and selection

• Introduction to neural networks
• Structure and working of a neural network.
• Activation functions
• Convolutional neural networks (CNN) and their applications
• Recurrent neural networks (RNN) and their applications

• Introduction to NLP
• Tokenization  stemming  and lemmatisation.
• Part-of-speech tagging
• Named entity recognition.
• Sentiment analysis

• Emerging trends in AI
• AI and its impact on society
• Ethical considerations in AI development and use
• Bias and fairness in AI
• Explainable AI

Cost: 4900 USD

Duration: 5 Days

• 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