• Free Transcript
• Free CPD Accredited PDF Certificate
• FREE Course: Machine Learning for Beginners
• FREE Course: Python Basic to Advanced for Data Science Online Course

• Grasp the basic concepts of artificial intelligence 
• Enhance your understanding of knowledge representation in AI
• Receive detailed lessons on machine learning 
• Learn the basic principles of building AI applications 
• Explore the vital aspects of deep learning, robotics and computer vision

• Affordable, well-structured and high-quality e-learning study materials
• Meticulously crafted, engaging and informative tutorial videos and materials
• Efficient exam systems for the assessment and instant result
• Earn UK & internationally recognised accredited qualification
• Easily access the course content on mobile, tablet, or desktop from anywhere, anytime
• Excellent career advancement opportunities
• Get 24/7 student support via email
• What Skills Will You Learn from This Course?
• Machine Learning 
• AI Applications
• Deep Learning 
• Robotics

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• AI Product Manager
• AI Consultant

Cost: 14 GBP

Duration: Upto 3 Hours

• ⇒ Course 01: Neuroscience of Learning
• ⇒ Course 02: Neurology
• ⇒ Course 03: Human Anatomy and Physiology
• ⇒ Course 04: Neuro-linguistic Programming (NLP) Diploma
• ⇒ Course 05: Neuropsychology and Development of Children
• ⇒ Course 06: Neuroplasticity: How To Rewire Your Brain
• ⇒ Course 07: Neuroscience for Personal Branding
• ⇒ Course 08: Pharmacology
• ⇒ Course 09: Genetics
• ⇒ Course 10: Essential Principles of Immunology
• ⇒ Course 11: Clinical Observations Skills
• ⇒ Course 12: Treating Post Traumatic Stress Disorder
• ⇒ Course 13: Bipolar Disorder
• ⇒ Course 14: Child Language Disorder (CLD)
• ⇒ Course 15: Concussion and Brain Injury Awareness
• ⇒ Course 16: Brain Fitness: Train Faster Thinking, Better Memory & Focus
• ⇒ Course 17: Cognitive Behavioural Therapy (CBT) Training
• ⇒ Course 18: Dialectal Behaviour Therapy (DBT)
• ⇒ Course 19: Paediatric First Aid Level 3
• ⇒ Course 20: Overcome Insomnia and Sleep Better
• ⇒ Course 21: Pain Management
• ⇒ Course 22: Cerebral Palsy Awareness
• ⇒ Course 23: Medical Terminology Training
• ⇒ Course 24: Basic Biology Course
• ⇒ Course 25: DNA and Genealogy Course
• ⇒ Course 26: Biomedical Science
• ⇒ Course 27: Medication Administration Level 4
• ⇒ Course 28: Play Therapy
• ⇒ Course 29: Dyslexia Awareness Course

• Standout by earning a CPD Accredited Certificate upon completion.
• Gain lifetime access to the course materials and keep your skills on-check anytime.
• Our tutors are available 24/7 to answer your questions throughout the program.
• The program has been designed to be clear and easy to understand Receive a free Student ID Card as part of the program (£10 applicable for international delivery).

• Module 1 Introduction to Neuroscience and Learning
• Module 2 Basics of Neuroscience
• Module 3 Neuroplasticity and Learning
• Module 4 Cognitive Development and Learning
• Module 5 Attention and Focus
• Module 6 Emotions and Learning
• Module 7 Motivation and Reward Systems
• Module 8 Learning Disorders and Neurodiversity
• Module 9 Brain-Based Learning Strategies

• PDF Certificate: Free (Previously it was £*30 = £)
• Hard Copy Certificate: Free (For The Title Course: Previously it was £)

Cost: 698 AED

Duration: Upto 150 Hours

• ⇒ Course 01: Working with Individuals with Learning Disabilities Level 2
• ⇒ Course 02: Learning Disability Awareness
• ⇒ Course 03: Understanding Learning Difficulties Level 3
• ⇒ Course 04: Learning Support Assistant
• ⇒ Course 05: SEN Support & Teaching Methodologies
• ⇒ Course 06: SENCO (Special Educational Needs Coordination) Training Online
• ⇒ Course 07: Applied Behaviour Analysis (ABA) Expert Training Certification
• ⇒ Course 08: Early Childhood: Child Care
• ⇒ Course 09: Play Therapy
• ⇒ Course 10: Understanding Autism Awareness and Diagnosis
• ⇒ Course 11: Child Psychology
• ⇒ Course 12: Assessing Mental Capacity
• ⇒ Course 13: Children and Adolescents Counselling Course
• ⇒ Course 14: Teaching Assistant
• ⇒ Course 15: Classroom Behaviour Management
• ⇒ Course 16: ADHD Awareness
• ⇒ Course 17: Phonics Teaching Assistant
• ⇒ Course 18: Dyslexia Awareness Course
• ⇒ Course 19: Remote Teaching Online
• ⇒ Course 20: Paediatric Care Course
• ⇒ Course 21: Mental Health and Psychiatry Training
• ⇒ Course 22: Safeguarding Children Level 3
• ⇒ Course 23: Primary Teacher
• ⇒ Course 24: Safeguarding Children: Internet Safety Course
• ⇒ Course 25: Youth Work & Leadership
• ⇒ Course 26: Cognitive Behavioural Therapy (CBT) Training
• ⇒ Course 27: Dialectal Behaviour Therapy (DBT)
• ⇒ Course 28: Child Playwork
• ⇒ Course 29: EYFS Teaching – Level 3
• ⇒ Course 30: Mental Health Nursing and Support Worker

• Standout by earning a CPD Accredited Certificate upon completion.
• Gain lifetime access to the course materials and keep your skills on-check anytime.
• Our tutors are available 24/7 to answer your questions throughout the program.
• The program has been designed to be clear and easy to understand Receive a free Student ID Card as part of the program (£10 applicable for international delivery).

• Working with Individuals with Learning Disabilities Level 2
• Module 01: Learning Support Assistant: Workplace & Work
• Module 02: Teaching Methodologies
• Module 03: Skills of a Learning Support Assistant
• Module 04: The Role and Responsibilities of the Learning Support Assistant
• Module 05: SEN Teaching
• Module 06: How to Support Pupils with Special Needs Part 1
• Module 07: How to Support Pupils with Special Needs Part 2
• Module 08: Safeguarding Children & Young People

• PDF Certificate: Free (Previously it was £*30 = £)
• Hard Copy Certificate: Free (For The Title Course: Previously it was £)

Cost: 698 AED

Duration: Upto 150 Hours

• 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

• Course 01: C# Programming
• Course 02: Stripe with C#
• Course 03: NET Core API Development
• Course 04: DevOps Fundamentals: Maven and SonarQube for Engineers
• Course 05: Front End Web Development Training for Everyone
• Course 06: HTML Web Development Crash Course
• Course 07: C++ Programming for Absolute Beginners
• Course 08: Computer Vision By Using C++ and OpenCV with GPU Support
• Course 09: Diploma in Computer Programming
• Course 10: Diploma in Python Programming
• Course 11: Data Science & Machine Learning with Python
• Course 12: Computer Science With Python
• Course 13: Code with Python: Learn Classes, Methods, and OOP
• Course 14: Spatial Data Visualization and Machine Learning in Python
• Course 15: Python Data Science with Numpy, Pandas, and Matplotlib
• Course 16: Machine Learning with Python Course
• Course 17: Learn AI with Python
• Course 18: JavaScript Foundations for Everyone
• Course 19: JavaScript Functions
• Course 20: Master JavaScript with Data Visualization
• Course 21: jQuery: JavaScript and AJAX Coding Bible
• Course 22: Refactor Javascript
• Course 23: Secure Programming of Web Applications – Developers and TPMs
• Course 24:  & WebGL 3D Programming
• Course 25: AutoCAD VBA Programming – Beginner Course
• Course 26: IT: Ethical Hacking & IT Security
• Course 27: Linux Security and Hardening
• Course 28: Network Security
• Course 29: WordPress Security Mastery
• Course 30: Cyber Security

• Master C# Programming and understand its core principles, from basic to advanced.
• Build secure, scalable web applications using C# Programming and .NET Core.
• Explore industry tools like Maven and SonarQube for code quality in DevOps practices.
• Develop strong front-end skills with HTML and JavaScript for responsive design.
• Gain knowledge in data science and machine learning, ideal for a wide range of tech roles.

• Get a Free CPD Accredited Certificate upon completion of this course
• Get a free student ID card with this Training program (£10 postal charge will be applicable for international delivery)
• This course is affordable and simple to understand
• This course is entirely online, interactive lesson with voiceover audio
• Get Lifetime access to the course materials
• This course comes with 24/7 tutor support

• Module 01: Introduction
• Module 02: Basics to Get Started
• Module 03: Logic
• Module 04: Methods (Basic)
• Module 05: Classes (Basics)
• Module 06: Variables (Basic)
• Module 07: Error Handling
• Module 08: Useful Methods
• Module 09: Text (Advanced)
• Module 10: Classes (Advanced)
• Module 11: Binary (Advanced)
• Module 12: Asynchronous (Advanced)
• Module 13: Search in Objects (LINQ)

• Module 01: Introduction
• Module 02: First Section
• Module 03: Stripe Checkout
• Module 04: Subscriptions
• Module 05: Misc.

• PDF Certificate: Free (Previously it was £*11 = £143)
• CPD Hard Copy Certificate: Free (For The First Course: Previously it was £)

• Beginners in programming who want a thorough grounding in C# Programming.
• Professionals seeking career advancement in fields like software development, IT security, and data science.
• Students and tech enthusiasts aiming to build competitive, market-relevant skills.
• Anyone keen to develop versatile programming skillset across multiple technologies.

• Software Developer – £35,000 to £65,000
• Web Application Developer – £30,000 to £55,000
• Data Scientist in Finance – £45,000 to £80,000
• Cyber Security Analyst – £40,000 to £75,000
• Machine Learning Engineer – £50,000 to £85,000

Cost: 680 AED

Discount: 90% Off for Laimoon Users!

Duration: Upto 130 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

• Spatial Data Handling: Learn to work with various types of spatial data using Python libraries like GeoPandas and Shapely.
• Data Visualization: Master the creation of stunning maps and spatial visualizations using libraries such as Matplotlib and Folium.
• Machine Learning for Spatial Data: Apply machine learning techniques to spatial problems using scikit-learn and other relevant libraries.
• Geospatial Analysis: Conduct advanced geospatial analysis, including spatial joins, buffers, and interpolation.
• Real-world Applications: Explore case studies in urban planning, environmental science, and business location analysis.

• Interdisciplinary Skills: Combine expertise in programming, data science, and geography.
• Project-Based Learning: Work on real-world spatial problems and build a portfolio of geospatial projects.
• Cutting-Edge Techniques: Learn the latest methods in spatial data science and machine learning.
• Course Design:
• Online Access: Study from any internet-enabled device.
• Interactive Jupyter Notebooks: Engage with code and explanations in an interactive environment.
• Datasets Provided: Work with curated spatial datasets to practice your skills.

• Age: Must be at least 18 years old.
• Language: Proficient understanding of English.
• Technical: Intermediate Python programming skills required.
• Education: Basic understanding of data science concepts beneficial.
• Equipment: Access to a computer with internet connection.

Cost: 6 GBP

Duration: Upto 4 Hours

• Understand the architecture and principles of deep neural networks.
• Implement deep learning models using libraries such as TensorFlow and PyTorch.
• Apply deep learning techniques to solve business challenges in areas such as image classification and sentiment analysis.
• Fine-tune pre-trained models for specific business domains and use cases.
• Explore advanced topics in deep learning, including generative adversarial networks (GANs) and reinforcement learning.
• Develop strategies for deploying and scaling deep learning solutions in production environments.

• Fundamentals of Neural Networks
• Introduction to TensorFlow and Keras
• Hands-on: Building Your First Neural Network

• Understanding CNN Architecture
• Image Classification with CNNs
• Case Studies: CNN Applications in Business

• Introduction to RNNs and LSTM Networks
• Natural Language Processing (NLP) with RNNs
• Hands-on: Sentiment Analysis with RNNs

• Introduction to GANs and Reinforcement Learning
• Case Studies: Advanced Deep Learning Applications
• Deploying Deep Learning Models in Production Environments.

Cost: 5995 USD

Duration: 4 Days

• Instant access to current information on Deep Learning & Neural-Networks Python- Keras Practice licences and business startups
• Participation in online maintenance exercises and troubleshooting
• Quick assessment and guidance for all theoretical repair assignments
• CPD accreditation for proof of acquired skills and knowledge
• Freedom to study in any location and at a pace that suits you
• Expert support from dedicated tutors committed to online learning

• Chance to earn a Prestigious CPD Accredited Certificate upon  completion of Deep Learning & Neural-Networks Python- Keras Practice
• Get a free student ID card with Deep Learning & Neural-Networks Python- Keras Practice
• Lifetime access to the Deep Learning & Neural-Networks Python- Keras Practice course materials
• Get instant access to this Deep Learning & Neural-Networks Python- Keras Practice course 
• Learn Deep Learning & Neural-Networks Python- Keras Practice from anywhere in the world
• 24/7 tutor support with the Deep Learning & Neural-Networks Python- Keras Practice course.
• Deep Learning & Neural-Networks Python- Keras Practice is an entirely online, interactive lesson with voiceover audio

• Course Introduction And Table Of Contents
• Deep Learning Overview
• Choosing Between ML Or DL For The Next AI Project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation And Sample Program To Test
• TensorFlow Library Installation And Sample Program To Test
• Keras Installation And Switching Theano And TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps And Terminology
• First Neural Network With Keras - Understanding Pima Indian Diabetes Dataset
• Explaining Training And Evaluation Concepts
• Pima Indian Model - Steps Explained
• Coding The Pima Indian Model
• Pima Indian Model - Performance Evaluation
• Pima Indian Model - Performance Evaluation - K-Fold Validation - Keras
• Pima Indian Model - Performance Evaluation - Hyper Parameters
• Understanding Iris Flower Multi-Class Dataset
• Developing The Iris Flower Multi-Class Model
• Understanding The Sonar Returns Dataset

• Deep Learning Engineer
• AI Research Scientist
• Computer Vision Engineer
• Natural Language Processing (NLP) Specialist
• Robotics Engineer

• Professionals seeking to enhance their Deep Learning & Neural-Networks Python- Keras Practice skills and knowledge.
• Individuals looking to transition into a Deep Learning & Neural-Networks Python- Keras Practice-related career.
• Anyone passionate about Deep Learning & Neural-Networks Python- Keras Practice and eager to gain practical expertise.

Cost: 46 AED

Duration: Upto 5 Hours

• Understand the fundamental concepts of deep learning and how it differs from traditional machine learning.
• Gain proficiency in using Keras, a powerful deep learning library, for building and training neural network models.
• Develop practical skills in creating and optimizing neural network models for different datasets, including image recognition tasks and regression problems.

• Course Introduction and Table of Contents
• Deep Learning Overview
• Choosing Between ML or DL for the next AI project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation and Sample Program to Test
• TensorFlow library Installation and Sample Program to Test
• Keras Installation and Switching Theano and TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps and Terminology
• First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
• Explaining Training and Evaluation Concepts
• Pima Indian Model - Steps Explained
• Coding the Pima Indian Model
• Pima Indian Model - Performance Evaluation
• Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
• Pima Indian Model - Performance Evaluation - Hyper Parameters
• Understanding Iris Flower Multi-Class Dataset
• Developing the Iris Flower Multi-Class Model
• Understanding the Sonar Returns Dataset
• Developing the Sonar Returns Model
• Sonar Performance Improvement - Data Preparation - Standardization
• Sonar Performance Improvement - Layer Tuning for Smaller Network
• Sonar Performance Improvement - Layer Tuning for Larger Network
• Understanding the Boston Housing Regression Dataset
• Developing the Boston Housing Baseline Model
• Boston Performance Improvement by Standardization
• Boston Performance Improvement by Deeper Network Tuning
• Boston Performance Improvement by Wider Network Tuning
• Save & Load the Trained Model as JSON File (Pima Indian Dataset)
• Save and Load Model as YAML File - Pima Indian Dataset
• Load and Predict using the Pima Indian Diabetes Model
• Load and Predict using the Iris Flower Multi-Class Model
• Load and Predict using the Sonar Returns Model
• Load and Predict using the Boston Housing Regression Model
• An Introduction to Checkpointing
• Checkpoint Neural Network Model Improvements
• Checkpoint Neural Network Best Model
• Loading the Saved Checkpoint
• Plotting Model Behavior History
• Dropout Regularization - Visible Layer
• Dropout Regularization - Hidden Layer
• Learning Rate Schedule using Ionosphere Dataset - Intro
• Time Based Learning Rate Schedule
• Drop Based Learning Rate Schedule
• Convolutional Neural Networks - Introduction
• MNIST Handwritten Digit Recognition Dataset
• MNIST Multi-Layer Perceptron Model Development
• Convolutional Neural Network Model using MNIST
• Large CNN using MNIST
• Load and Predict using the MNIST CNN Model
• Introduction to Image Augmentation using Keras
• Augmentation using Sample Wise Standardization
• Augmentation using Feature Wise Standardization & ZCA Whitening
• Augmentation using Rotation and Flipping
• Saving Augmentation
• CIFAR-10 Object Recognition Dataset - Understanding and Loading
• Simple CNN using CIFAR-10 Dataset
• Train and Save CIFAR-10 Model
• Load and Predict using CIFAR-10 CNN Model
• RECOMENDED READINGS

• Unlimited access to the course for forever
• Digital Certificate, Transcript, student ID all included in the price
• Absolutely no hidden fees
• Directly receive CPD accredited qualifications after course completion
• Receive one to one assistance on every weekday from professionals
• Immediately receive the PDF certificate after passing
• Receive the original copies of your certificate and transcript on the next working day
• Easily learn the skills and knowledge from the comfort of your home

Cost: 89 AED

Discount: 95% Off for Laimoon Users!

Duration: Upto 11 Hours

• Unleash the potential of heuristic methodologies in deep learning using R.
• Construct and optimize advanced neural networks for diverse data sets.
• Interpret and apply cutting-edge heuristic algorithms to solve complex real-world problems.
• Demonstrate proficiency in implementing R-based solutions for efficient model training and evaluation.
• Cultivate a strategic mindset to innovate and adapt heuristic approaches for dynamic data landscapes.

• Section 01: Experimental Design - Heuristics Project 1-Agriculture
• Section 02: Experimental Design - Heuristics Project 2-Cryptocurrencies
• Section 03: Experimental Design: Heuristics Project 3-Energy Sector
• Section 04: Experimental Design: Heuristics Project 4-Financial Markets

Cost: 50 AED

Duration: Upto 4 Hours

• Gain a comprehensive understanding of R's application in constructing and optimizing neural networks.
• Master the art of data preprocessing and feature engineering for enhanced model performance.
• Explore cutting-edge techniques in deep learning, from convolutional to recurrent neural networks.
• Acquire proficiency in leveraging R's capabilities for complex tasks like image recognition and natural language processing.
• Develop the skills to implement and fine-tune neural networks, paving the way for innovation in diverse data-driven domains.

• Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1)
• Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2)
• Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1)
• Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2)

Cost: 50 AED

Duration: Upto 3 Hours

• Master the architecture and principles of artificial neural networks.
• Develop proficiency in implementing advanced deep learning techniques.
• Acquire the skills to optimize neural networks for enhanced performance.
• Explore real-world applications of deep learning in diverse industries.
• Foster critical thinking to innovate and adapt neural networks for evolving challenges.

• Section 01: Introduction
• Section 02: ANN Installation
• Section 03: Data Preprocessing
• Section 04: Data Encoding
• Section 05: Steps to Build ANN
• Section 06: Predictions and Imbalance-Learn

Cost: 50 AED

Duration: Upto 3 Hours

• Master the fundamentals of Deep Learning and Neural Networks in Python.
• Acquire proficiency in implementing advanced algorithms for AI applications.
• Develop the skills to design and optimize neural network architectures.
• Gain insights into real-world applications and case studies in the field.
• Enhance problem-solving abilities through hands-on projects and challenges.

• Course Introduction and Table of Contents
• Deep Learning Overview
• Choosing Between ML or DL for the next AI project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation and Sample Program to Test
• TensorFlow library Installation and Sample Program to Test
• Keras Installation and Switching Theano and TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps and Terminology

Cost: 50 AED

Duration: Upto 12 Hours

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• You have just found Keras.
• Multi-backend Keras and
• Guiding principles
• Installation
• Configuring Keras backend

• Sequential model
• Functional API

• About Keras models
• Sequential
• Model (functional API)

• About Keras layers
• Core Layers
• Convolutional Layers
• Pooling Layers
• Locally-connected Layers
• Recurrent Layers
• Embedding Layers
• Merge Layers
• Advanced Activations Layers
• Normalization Layers
• Noise layers
• Layer wrappers
• Writing your own Keras layers

• Sequence Preprocessing
• Text Preprocessing
• Image Preprocessing
• Losses
• Metrics
• Optimizers
• Activations
• Callbacks
• Datasets
• Applications
• Backend
• Initializers
• Regularizers
• Constraints
• Visualization
• Scikit-learn API
• Utils

Cost: 49 USD

Duration: 1 To 2 Months

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• Software Installation

• Introduction
• Keras Backends
• Design and Compile a Model
• Model Training, Evaluation, and Prediction
• Training with Data Augmentation
• Training with Transfer Learning and Data Augmentation

• Introduction to TensorFlow
• Introduction to TensorBoard
• Types of Parallelism in Deep Learning – Synchronous and Asynchronous
• Distributed TensorFlow
• Configuring Keras to use TensorFlow for Distributed Problems

• Introduction
• Introduction to Google Cloud Machine Learning Engine
• Datasets, Feature Columns, and Estimators
• Representing Data in TensorFlow
• Quick Dive into TensorFlow Estimators
• Creating Data Input Pipelines
• Setting Up Our Estimator
• Packaging Our Model
• Training with Google Cloud ML Engine
• Hyperparameter Tuning in the Cloud
• Deploying Our Model for Prediction
• Creating Our Prediction API

• TensorFlow for Building Deep Learning Models
• Basic Syntaxes, Function Optimization, Variables, and Placeholders
• TensorBoard for Visualization

• Start by Loading the Imported Dataset
• Building the Layers of the Neural Network in TensorFlow
• Optimizing the Softmax Cross Entropy Function
• Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not

• Importing the Two Datasets Using TensorFlow and Sklearn API
• Writing the TensorFlow Code to Add Convolutional and Pooling Layers
• Using API to Optimize CNN
• Implementing CNN to Create a Face Recognition System

• Understanding the RNN and the Need for LSTM
• Implementing RNN
• Monthly Riverflow Prediction of Turtle River in Ontario
• Implement LSTM Project to Predict Decimal Number of Given Binary Representation

• Encoder and Decoder for Efficient Data Representation
• TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
• Using Stacked Autoencoders for Representation on MNIST Dataset
• Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset

• Generative Adversarial Networks for Creating Synthetic Dataset
• Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
• Coding the Generator and Discriminator Using TensorFlow
• Training GANs to Create Synthetic GPS Based Trajectories

Cost: 49 USD

Duration: 1 To 2 Months

• Government Certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• Course Overview
• Installation and Setup
• Lesson Overview
• Data Representation
• Loading a Dataset from the UCI Machine Learning Repository
• Data Pre-Processing
• Cleaning the Data
• Appropriate Representation of the Data
• Lifecycle of Model Creation
• Machine Learning Libraries and scikit-learn
• Keras
• Model Training
• Creating a Simple Model
• Model Tuning
• Regularization

• Lesson Overview
• Introduction to ANNs
• Linear Transformations
• Matrix Transposition
• Introduction to Keras

• Lesson Overview
• Building Your First Neural Network
• Gradient Descent for Learning the Parameters
• Model Evaluation

• Lesson Overview
• Cross-Validation
• Cross-Validation for Deep Learning Models
• Evaluate Deep Neural Networks with Cross-Validation
• Model Selection with Cross-validation
• Write User-Defined Functions to Implement Deep Learning Models with Cross-Validation

• Lesson Overview
• Regularization
• L1 and L2 Regularization
• Dropout Regularization
• Other Regularization Methods
• Data Augmentation
• Hyperparameter Tuning with scikit-learn

• Lesson Overview
• Accuracy
• Imbalanced Datasets
• Confusion Matrix
• Computing Accuracy and Null Accuracy with Healthcare Data
• Calculate the ROC and AUC Curves

• Lesson Overview
• Computer Vision
• Architecture of a CNN
• Image Augmentation
• Amending Our Model by Reverting to the Sigmoid Activation Function
• Changing the Optimizer from Adam to SGD
• Classifying a New Image

• Lesson Overview
• Pre-Trained Sets and Transfer Learning
• Fine Tuning a Pre-Trained Network
• Classification of Images that are not Present in the ImageNet Database
• Fine-Tune the VGG16 Model
• Image Classification with ResNet

• Lesson Overview
• Sequential Memory and Sequential Modeling
• Long Short-Term Memory – LSTM
• Predict the Trend of Apple's Stock Price Using an LSTM with 50 Units (Neurons)
• Predicting the Trend of Apple's Stock Price Using an LSTM with 100 Units

Cost: 49 USD

Duration: 1 To 2 Months

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• Set Up Caffe2 on Linux
• Understanding the Caffe2 Architecture
• Transitioning from Machine Learning to Deep Learning
• Running an Image Classifier Using Caffe2

• Learn about Matrices Using Python – NumPy
• Understanding and Implementing Logistic Regression and Neural Networks
• Understanding and Implementing Deep Neural Networks

• Caffe2 Introduction
• Caffe2 Python Wrapper
• Mathematical Operators in Caffe2
• Network Creators and Assisters in Caffe2 – Part 1
• Network Creators and Assisters in Caffe2 – Part 2
• Network Creators and Assisters in Caffe2 – Part 3

• How Machines Learn to See!
• Introduction to Convolutional Neural Networks
• Implement a Convolution Layer Using Caffe2
• Pooling Layer and Dropout in Caffe2
• Role of Activation Functions in Solving Non-Linear Optimization

• Machine Learning Strategy
• How to Perform Data Selection, Preparation, and Processing
• Regularization of Neural Networks
• Optimizing Neural Networks
• Optimization Algorithms

• Sequence Learning
• Introduction to Recurrent Neural Networks
• LSTMs – A Special Case of RNNs
• Learning about Word Embeddings
• Introduction to Augmented Recurrent Neural Networks

• Why Deep Learning?
• Machine Learning Categories
• Why Caffe2?
• Install and Set Up Caffe2
• Build a Caffe2 Docker

• Definition of a Computational Graph Through Examples
• Introduce Workspace, Operators, and Nets
• Working with Computational Graphs

• Housing Price Prediction
• Representing a Linear Regression Model in a Computational Graph
• Training Procedure
• Training a Linear Regression Model

• Fashion Product Recognition Problem
• What Is Supervised Learning?
• What Is Transfer Learning?
• Model Zoo in Caffe2
• Fine-Tune a Model for Recognizing Fashion Products

• Chatbot Customer Service
• What Is Sequence-to-Sequence Learning?
• What Are RNNs and LSTMs?
• Training an RNN-Based Model to Write like Shakespeare

• Why Deep Reinforcement Learning?
• What Is Deep Reinforcement Learning?
• What Is Deep Q-Network?
• Training a Deep Q- Network for Solving the Cart-Pole Problem

• AI on Mobile Devices Using Face ID
• Challenges in Running AI Models on Mobile Devices
• SequeezeNet
• Deploy SequeezeNet on a Mobile Device

Cost: 49 USD

Duration: 1 To 2 Months

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• The Course Overview
• Introduction to PyTorch
• Installing PyTorch on Linux and Windows
• Installing CUDA
• Introduction to Tensors and Variables
• Working with PyTorch and NumPy
• Working with PyTorch and GPU
• Handling Datasets in PyTorch
• Deep Learning Using PyTorch

• Building a Simple Neural Network
• Loss Functions in PyTorch
• Optimizers in PyTorch
• Training the Neural Network
• Saving and Loading a Trained Neural Network
• Training the Neural Network on a GPU

• Computer Vision Motivation
• Convolutional Neural Networks
• The Convolution Operation
• Concepts - Strides, Padding, and Pooling
• Loading and Using MNIST Dataset
• Building the Model
• Training and Testing

• Sequence Models Motivation
• Word Embedding
• Recurrent Neural Networks
• Building a Text Generation Model in PyTorch
• Training and Testing

• Autoencoders Motivation
• How Autoencoders Work
• Types of Autoencoders
• Building Denoising Autoencoder Using PyTorch
• Training and Testing

• Reinforcement Learning Motivation
• Reinforcement Learning Concepts
• DQN, Experience Replay
• The OpenAI Gym Environment
• Building the Cartpole Agent Using DQN
• Training and Testing

• The Course Overview
• What Makes PyTorch Special?
• Installing PyTorch

• Problem: Detect a Specific Type of Object in an Image
• Quick Win: Using a Pretrained AlexNet Model for Beaver Detection
• Getting and Preparing Image Data
• Building, Training, and Testing Your Model
• Using Your Model to Detect Beavers and What’s Next?

• Problem: Recognize the Language of a Specific Text
• Understanding and Preparing Language Data
• Building, Training, and Testing Your Model for Language Detection
• Using Your Model to Detect Languages and What’s Next?

• Problem: Translate a Specific Text from One Language to Another
• Understanding and Preparing Dataset for Language Translation
• Building, Training, and Testing Your Models for Language Translation
• Using Your Models for Language Translation

• Problem: Extract Key Style Features from One Image and Use It on Another One
• Preparing Images for Style Transfer
• Building and Training Style Transfer Model

Cost: 49 USD

Duration: 1 To 2 Months

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

• The Course Overview
• A Brief History of Deep Learning
• Deep Learning Today
• Tools, Requirements, and Setup

• Exploring Supervised Learning
• Representational Learning and Feature Engineering
• Linear Regression
• The Perceptron

• Feedforward Networks
• Backpropagation
• Neural Networks from Scratch
• Overfitting and Regularization

• Understanding CNNs
• Implementing a CNN
• Deep CNNs

• Very Deep CNNs
• Batch Normalization
• Fine-Tuning

• Semantic Segmentation
• Fully Convolutional Networks

• Recurrent Neural Networks
• LSTM and Advancements

• Building a CNN to Detect General Images
• Training and Deploying on a Cluster

• Comparison of DL Frameworks
• Exciting Areas for Upcoming Research

• The Course Overview
• Fundamentals of Neural Networks
• Training Deep Neural Networks
• Using Forward Propagation, Backprop, and SGD
• Logistic Regression with a Neural Network Mindset
• Convolutional Neural Network Handwriting Recognition

• Working with MxNet and Gluon
• Defining and Training Neural Networks in MxNet/Gluon
• Working with TensorFlow and Keras
• Defining and Training Neural Networks in Keras/TensorFlow
• Comparing the Two Frameworks
• Mini Project - CIFAR Classification

• Weight Initialization for Deep Networks
• Regularization and Dropout
• Normalizing and Vanishing/Exploding Gradients
• Mini Project – SIGNS Dataset

• Understanding Stochastic Gradient Descent
• Adaptive Learning Algorithms - RMSProp and Adam
• Mini Project - Language Modeling

• Hyperparameters
• Tuning Hyperparameters - Grid Search
• Tuning Hyperparameters - Random Search
• Mini Project -Music Synthesis

Cost: 49 USD

Duration: 1 To 2 Months

• Master Python's role in data manipulation and analysis.
• Acquire proficiency in machine learning algorithms and applications.
• Demonstrate competence in data visualization techniques.
• Develop skills to tackle real-world data science challenges.
• Create and deploy machine learning models for practical solutions.

• Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
• Introduction to Machine Learning - Part 2 - Classifications and Applications
• System and Environment Preparation - Part 1
• System and Environment Preparation - Part 2
• Learn Basics of Python - Assignment
• Learn Basics of Python - Assignment
• Learn Basics of Python - Functions
• Learn Basics of Python - Data Structures
• Learn Basics of NumPy - NumPy Array
• Learn Basics of NumPy - NumPy Data
• Learn Basics of NumPy - NumPy Arithmetic

Cost: 50 AED

Duration: Upto 11 Hours

• Free Time Management Course
• Free Exam
• Lifetime Access

• Students
• Job seekers
• Teachers

Cost: 89 AED

Discount: 95% off through Laimoon!

Duration: Upto 4 Hours

• Free Time Management Course
• Free Exam
• Lifetime Access

• Students
• Job seekers
• Teachers

Cost: 89 AED

Discount: 95% off through Laimoon!

Duration: Upto 4 Hours

• Proficiency in Python programming for data science.
• Competence in data manipulation using Pandas and NumPy.
• Advanced data visualization with Matplotlib.
• Handling missing data and data cleaning.
• Data aggregation and transformation.
• Working with various data formats (CSV, JSON).
• Exploring hierarchical indexing and pivot tables.
• Implementing data analysis and statistical techniques.
• Creating impactful data visualizations.
• Real-world data analysis projects.

• We use the very latest tools and applications in online education to teach you The Data Science with Python in exciting and innovative ways.
• You’ll be able to take the Data Science with Python course anywhere. Learn on your laptop, be taught on your tablet, school yourself on your smartphone, and practise on your PC. The choice is yours!
• Is time short? Study at the hours that suit you. As long as you have an online device, you can learn at any time of the day or night.

• Course Introduction and Table of Contents
• Introduction to Python, Pandas and Numpy
• System and Environment Setup
• Python Strings
• Python Numbers and Operators
• Python Lists
• Tuples in Python
• Sets in Python
• Python Dictionary
• NumPy Library - Introduction
• NumPy Array Operations and Indexing
• NumPy Multi-Dimensional Arrays
• Introduction to Pandas Series
• Introduction to Pandas Dataframes
• Pandas Dataframe conversion and drop
• Pandas Dataframe summary and selection
• Pandas Missing Data Management and Sorting
• Pandas Hierarchical-Multi Indexing
• Pandas CSV File Read Write
• Pandas JSON File Read Write
• Pandas Concatenation Merging and Joining
• Pandas Stacking and Pivoting
• Pandas Duplicate Data Management
• Pandas Mapping
• Pandas Grouping
• Pandas Aggregation
• Pandas Binning or Bucketing
• Pandas Re-index and Rename
• Pandas Replace Values
• Pandas Dataframe Metrics
• Pandas Random Permutation
• Pandas Excel sheet Import
• Pandas Condition Selection and Lambda Function
• Pandas Ranks Min Max
• Pandas Cross Tabulation
• Matplotlib Graphs and plots
• Matplotlib Histograms

Cost: 19 GBP

Duration: Upto 6 Hours

• Grasp the foundations of spatial data analytics.
• Acquire expertise in data preparation and visualization.
• Apply machine learning to spatial datasets.
• Construct an interactive analytics dashboard.
• Develop a functional dashboard server.
• Access and understand project source code.
• Interpret spatial data patterns.
• Utilize geospatial visualization techniques.
• Implement machine learning for spatial predictions.
• Leverage spatial data for decision-making.
•  

• Aspiring Spatial Data Analysts
• GIS Enthusiasts
• Data Science Professionals
• Geography and Environmental Science Students
• Anyone Passionate About Spatial Analytics

• Affordable, engaging & high-quality e-learning study materials.
• Tutorial videos/materials from the industry-leading experts.
• Study in a user-friendly, advanced online learning platform.
• Efficient exam systems for the assessment and instant result.
• The UK & internationally recognized accredited qualification.
• Access to course content on mobile, tablet, or desktop from anywhere, anytime.
• The benefit of career advancement opportunities.
• 24/7 student support via ; 

• Section 01: Introduction
• Section 02: Setup and Installations
• Section 03: Data Preparation
• Section 04: Data Visualization
• Section 05: Machine Learning
• Section 06: Building the Dashboard
• Section 07: Creating the Dashboard Server
• Section 08: Project Source Code

Cost: 70 AED

Duration: Upto 5 Hours

• Through this comprehensive course, you should be able to:
• Understand the fundamental concepts of deep learning and artificial neural networks.
• Install and configure an artificial neural network framework.
• Preprocess and structure data for optimal model performance.
• Encode data effectively for neural network training and predictions.
• Build and deploy artificial neural networks for real-world applications.
• Address data imbalance challenges and optimise model accuracy.

• Introduction
• ANN Installation
• Data Preprocessing
• Data Encoding
• Steps to Build ANN
• Predictions and Imbalance-Learn

• Data scientists and machine learning practitioners seeking to expand their knowledge.
• Software engineers interested in leveraging deep learning techniques.
• Students pursuing a career in artificial intelligence and machine learning.
• Professionals looking to enhance their skills in neural network development.
• Individuals with a passion for exploring advanced machine learning techniques.

Cost: 49 AED

Discount: 95% Off for Laimoon Users!

Duration: Upto 2 Hours

• Free Time Management Course
• Free Certificate
• Lifetime Access

• Students
• Job seekers
• Teachers

• Lecture 1: Introduction to Artificial Intelligence
• Lecture 2: Machine Learning
• Lecture 3: Neural Networks
• Lecture 4: Natural Language Processing (NLP)
• Lecture 5: Computer Vision
• Lecture 6: Ethics and Societal Implications of AI

Cost: 19 GBP

Discount: 90% off through Laimoon!

Duration: Upto 4 Hours

• Free Time Management Course
• Free Certificate
• Lifetime Access

• Students
• Job seekers
• Teachers

• Introduction
• Lesson 1: Why is learning to learn so important?
• Lesson 2: How does learning happen?
• Lesson 3: Where does learning come from?
• Lesson 4: What approaches to learning are there?
• Lesson 5: What can get in the way of learning?
• Lesson 6: How can we make learning happen?
• End Matter

Cost: 19 GBP

Discount: 95% Off for Laimoon Users!

Duration: Upto 4 Hours

Cost: 19 GBP

Discount: 95% Off for Laimoon Users!

Duration: Upto 3 Hours

Cost: 18 GBP

Discount: 90% Off for Laimoon Users!

Duration: Upto 3 Hours

• Unlimited lifetime access
• Instructor-led distance learning
• Get trained by industry experts
• 24/7 student support system
• Learn at your own pace and leisure
• Effective training, written and designed by certified professionals

• Equip yourself with the essential skills, confidence and competence to assist you in your chosen industry.

• Learn the most in-demand skills, get certified and level up your career. This online training course is designed to help you reach your professional goals.

• Earn industry-recognised credentials to demonstrate your new skills and add extra value to your CV.

Cost: 13 GBP

Discount: 90% Off for Laimoon Users!

Duration: Upto 13 Hours

• Programming Basics
• Python Data Types
• Structures and conditional statements
• Python core packages
• Introduction to Jupyter Notebook
• Introduction to Numpy and Pandas
• Data filtering and selecting
• Find duplicates and treat missing values
• Concatenate and transform data

• Overview of Supervised and Unsupervised Machine Learning
• Linear Regression
• Clustering with K-means
• Naive Bayes Classification
• Introduction to Neural Networks

• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchical Clustering concept and application

• Basic Statistics and Exploratory Analysis
• Descriptive summary statistics with Numpy
• Summarize continuous and categorical data
• Outlier analysis

• Advanced-Data Mugging
• Outlier Analysis
• Treating for missing values
• Normalization vs Standardization of data

• Setting up the project with ML workflow.
• Data Preprocessing and statistical exploration
• Building, Training, and evaluation of Machine Learning Model

• Analysis of the case study: Property Estimations
• Setting up the project with ML workflow
• Data Preprocessing and statistical exploration
• Choosing the Machine Learning Algorithm for this case
• Building, Training, and evaluation of Machine Learning Model

• Understand the principles and applications of Machine Learning
• Explore the utilization of Machine Learning through Python
• Analyze, manipulate, and visualize data using Python libraries
• Design Machine Learning algorithm
• Comprehend and analyze data structures and intricate logic
• Optimize predictive performance with multiple learning algorithms

• Our Machine Learning with Python 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: 40 Hours

• 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

The program was carefully designed by our team with industry and academic expertise to ensure participants learn the foundational knowledge they need while engaging in real-world projects and challenges that help them attain the skills needed for today's industry job roles.

During the 12 weeks, you will: 

• Master fundamental concepts and the language of Machine Learning
• Learn how to best represent yourself to recruiters in the Machine Learning space
• Design advanced Machine Learning solutions
• Work in a team to solve a real-world, research-oriented challenge
• Become proficient with the foundational libraries for Data Science and Machine Learning in Python
• Discover state of the art models and technique under investigation in today's Machine Learning research community
• Gain the skills to become a sufficient self-learner and investigator in the field of Machine Learning
• Learn the best practices for Machine Learning in development and in research settings
• Be guided by AI instructors and engineers to learn from real-life practitioners
• Gain the soft skills to become a full-stack fit in a diverse Data Science team in the job market

Cost: 2967 USD

Duration: 12 Weeks

• Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
• How to create resume and land your first job as a Data Scientist
• How to write complex Python programs for practical industry scenarios
• Learn to use NumPy for Numerical Data
• Supervised vs Unsupervised Machine Learning
• Machine Learning Concepts and Algorithms
• Use Python to clean, analyze, and visualize data
• Statistics for Data Science
• Learn data cleaning, processing, wrangling and manipulation
• How to use Python for Data Science
• Learn Plotting in Python (graphs, charts, plots, histograms etc)
• Machine Learning and it's various practical applications
• Learn Regression, Classification, Clustering and Sci-kit learn
• K-Means Clustering
• Building Custom Data Solutions
• Probability and Hypothesis Testing

• This course is ideal for those who work in or aspire to work in the following professions:
• Data Scientist
• Data Analyst
• Software Developer
• Data Engineer
• Anyone who wants to learn Data Science & Machine Learning with Python.

• Accredited by CPD
• Conducted by industry experts
• Get Instant E-certificate
• Fully online, interactive course with Professional voice-over
• Developed by qualified professionals
• Self paced learning and laptop, tablet, smartphone friendly
• 24/7 Tutor Support

Cost: 18 GBP

Discount: 90% Off for Laimoon Users!

Duration: Upto 22 Hours

• Boost your career in learning to learn
• Deepen your knowledge and skills in your chosen field just in hours not years!
• Study a course that has been designed by industry professionals.
• Save money and time by studying your convenient time
• Have access to a tutor whenever you are in need

• Lifetime Access
• High-quality e-learning study materials 
• The UK and globally recognized qualification
• Self-paced, no fixed schedules
• 24/7 customer support through email
• Available to students anywhere in the world
• No hidden fee
• Study in a user-friendly, advanced online learning platform

Cost: 20 GBP

Discount: 97% off through Laimoon!

Duration: Upto 3 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