Machine Learning Training in the UAE

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab.

By the end of this training, participants will be able to:

• Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This instructor-led, live training in the UAE (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimize chatbot performance over time.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

By the end of this training, participants will be able to:

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.

By the end of this training, participants will be able to:

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-powered autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

By the end of this training, participants will be able to:

• Analyze production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimize production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

By the end of this training, participants will be able to:

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling.
• Use neural networks for classification and prediction tasks.
• Perform sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.

By the end of this training, participants will be able to:

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.

By the end of this training, participants will be able to:

• Understand AI methodologies for process optimization in chip fabrication.
• Implement AI models to enhance yield and reduce defects.
• Analyze process data to identify key parameters for optimization.
• Apply machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

By the end of this training, participants will be able to:

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

By the end of this training, participants will be able to:

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate their skills in AI-driven threat detection and incident response.

By the end of this training, participants will be able to:

• Implement advanced AI algorithms for real-time threat detection.
• Customize AI models for specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.

By the end of this training, participants will be able to:

• Understand AI applications in cybersecurity.
• Implement AI algorithms for threat detection.
• Automate incident response with AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in the UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.

By the end of this training, participants will be able to:

• Install and configure Weka.
• Understand the Weka environment and workbench.
• Perform data mining tasks using Weka.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This course aims to equip participants with foundational skills in utilizing Machine Learning techniques in real-world scenarios. By leveraging Python programming language and its diverse libraries, alongside numerous practical examples, learners will be guided on how to effectively use key components of Machine Learning, make informed data modeling choices, interpret algorithm outputs, and verify the accuracy of results.

Our objective is to empower you with the confidence to comprehend and apply essential Machine Learning tools while steering clear of typical missteps in Data Science implementations.

This course aims to enhance your practical proficiency in utilizing Machine Learning techniques. By leveraging the Python programming language and its extensive libraries, along with numerous real-world examples, you will learn to effectively use key components of Machine Learning, make informed data modeling choices, interpret algorithm outputs, and verify results.

Our objective is to equip you with the confidence to understand and apply essential Machine Learning tools while steering clear of typical Data Science challenges.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training in the UAE (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.

By the end of this training, participants will be able to:

• Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
• Train high quality machine learning models.
• Efficiently solve different types of supervised machine learning problems.
• Write just the necessary code to initiate the automated machine learning process.

This course will blend theoretical concepts with hands-on practice, utilizing specific examples throughout the session.

This instructor-led, live training in the UAE (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

By the end of this training, participants will be able to:

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models.

By the end of this training, participants will be able to:

• Set up an optimal development environment, including all needed Python packages.
• Obtain important insights by analyzing the features of a data set.
• Optimize machine learning models through adaptation of the raw data itself.
• Clean and transform data sets in preparation for machine learning.

In the realm of Artificial Intelligence, machine learning enables computers to acquire knowledge without explicit programming.

Deep learning is a specialized area within machine learning that relies on techniques such as neural networks to learn from data representations and structures.

Python stands out as a high-level programming language known for its clear syntax and easy-to-read code.

This instructor-led, live training session will guide participants through the process of developing deep learning models for telecommunications by creating a deep learning credit risk model step-by-step using Python.

Upon completion of this course, attendees will be able to:

• Grasp the core principles of deep learning.
• Explore how deep learning is applied in telecommunications.
• Employ Python, Keras, and TensorFlow to develop deep learning models for telecom purposes.
• Create their own deep learning model for predicting customer churn using Python.

Course Format

• Engaging lectures and discussions.
• Abundant exercises and practice sessions.
• Practical implementation in a live-lab setting.

Customization Options for the Course

• To tailor this course to your specific needs, please contact us to make arrangements.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

By the end of this training, participants will be able to:

• Explore the AutoML product line to implement different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.

This instructor-led, live training in the UAE (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

• Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in the UAE (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

Machine Learning is a subset of Artificial Intelligence where computers can learn autonomously without explicit programming. Python, known for its clear syntax and readability, provides an outstanding array of well-tested libraries and techniques essential for developing machine learning applications.

This instructor-led live training will guide participants through the application of machine learning techniques and tools to address real-world challenges in the banking sector.

Participants will start by understanding core principles before applying their knowledge practically by constructing their own machine learning models. They will then utilize these models to undertake several team projects.

Audience

• Developers
• Data scientists

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

This instructor-led, live training in the UAE (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

• Understand the evolution and trends for machine learning.
• Know how machine learning is being used across different industries.
• Become familiar with the tools, skills and services available to implement machine learning within an organization.
• Understand how machine learning can be used to enhance data mining and analysis.
• Learn what a data middle backend is, and how it is being used by businesses.
• Understand the role that big data and intelligent applications are playing across industries.

This training program is designed for individuals who wish to implement Machine Learning in real-world applications within their teams. The course will focus on fundamental concepts and their practical business and operational uses, without delving into complex technical details.

Intended Participants

1. Investors and AI entrepreneurs
2. Managers and Engineers whose organizations are entering the AI domain
3. Business Analysts & Investors

Machine learning is a subset of Artificial Intelligence that enables computers to learn without explicit programming. Python, renowned for its clear syntax and readability, provides an extensive collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led live training session, participants will learn how to apply machine learning techniques and tools to address real-world challenges in the finance sector.

Participants will first grasp the core principles before applying their knowledge by creating their own machine learning models and working on several team projects.

By the end of this training, participants will be able to:

• Master the foundational concepts of machine learning
• Understand the applications and uses of machine learning in finance
• Create an algorithmic trading strategy using Python-based machine learning techniques

Audience

• Developers
• Data scientists

Format of the course

• A combination of lectures, discussions, exercises, and extensive hands-on practice

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

By the end of this training, participants will be able to:

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This training program is designed for individuals who wish to implement fundamental Machine Learning techniques in real-world applications.

Audience

Data scientists and statisticians with some experience in machine learning and proficiency in programming R. The focus of this course is on the practical aspects of data and model preparation, execution, post-analysis, and visualization. The aim is to provide a practical introduction to machine learning for participants who are keen on utilizing these methods in their professional roles.

Sector-specific examples are utilized to ensure the training is pertinent to the audience.

In this instructor-led live training session, participants will learn to utilize the iOS Machine Learning (ML) technology stack by progressing through the development and deployment of an iOS mobile application.

By the conclusion of this training, participants will be able to:

• Develop a mobile app with capabilities for image processing, text analysis, and speech recognition
• Incorporate pre-trained ML models into iOS applications
• Design a personalized ML model
• Integrate Siri Voice support within iOS apps
• Familiarize themselves with and apply frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
• Leverage programming languages and tools including Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder

Target Audience

• Software Developers

Course Structure

• A blend of lectures, discussions, practical exercises, and extensive hands-on practice

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level business and technical professionals who wish to apply machine learning techniques to solve real-world business challenges using practical case studies and hands-on tools.

By the end of this training, participants will be able to:

• Understand how machine learning fits into modern AI systems and business strategies.
• Identify appropriate machine learning methods for different business problems.
• Preprocess and transform business data for machine learning tasks.
• Apply core machine learning techniques such as classification, regression, clustering, and time series forecasting.
• Interpret and evaluate machine learning models in the context of business decision-making.
• Gain hands-on experience through case studies and apply learned techniques to practical scenarios.

This course provides an introduction to machine learning techniques as applied to robotics.

It offers a comprehensive overview of current methods, their motivations, and key concepts within the realm of pattern recognition.

Following a brief theoretical foundation, participants will engage in basic exercises using open-source software (typically R) or other widely-used tools.

Pattern Matching is a method employed to identify specific patterns within an image. It helps ascertain whether certain features are present in a captured image, such as identifying the expected label on a faulty product on an assembly line or verifying the precise dimensions of a part. This technique differs from "Pattern Recognition," which identifies general patterns based on extensive collections of related samples, by specifically defining what needs to be found and then indicating if the anticipated pattern is present.

Course Format

• This course covers the methodologies, technologies, and algorithms utilized in the realm of pattern matching as it pertains to Machine Vision.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start building machine learning models with Random forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

RapidMiner is an open-source data science software platform designed for quick application prototyping and development. It offers an integrated environment for tasks such as data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training session, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

By the end of this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Mash up data and create predictive models
• Integrate predictive analytics into business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange.