Swatar, City Centre

Regus Swatar, City Centre, 2nd Floor, Tower Business Centre, Swatar, Malta, BKR4013

The Swatar City Centre is on the second floor of Tower Business Centre, in the Swatar suburb of Birkirkara, a key commercial district. On the same road is the HSBC global call centre. Malta is a popular location for back office services, especially call centres, because of its reliable telecoms infrastructure and English speaking population. The Swatar City Centre is near major financial services, offshore banks and other leading companies. The country's main industries are expanding from tourism and freight to diverse manufacturing including pharmaceuticals, electronics, rubber, plastics, packaging, security systems, marine products and biomedical equipment. The centre is opposite a new state-of-the-art hospital, also a major interchange for the public transport network. Swatar borders on the university town of Msida and the centre's location near Birkirkara bypass makes it convenient for Malta International Airport and the capital city Valletta. There is a public garden, named Swatar Garden, which is situated at Balliju Guttenberg Street. The garden is equipped with European standard facilities such as; a 5-a-side football ground, energy efficient lightning and safety and security measures

Subcategories (10)

Deep Learning

Deep Learning
Fine-Tuning

Fine-Tuning
Federated Learning

Federated Learning
Reinforcement Learning

Reinforcement Learning
TinyML

TinyML
Explainable AI (XAI)

Explainable AI (XAI)
MLOps

MLOps
Supervised Learning

Supervised Learning
Unsupervised Learning

Unsupervised Learning
Ensemble Learning

Ensemble Learning

Explore Our Courses

Introduction to Pre-trained Models

 14 Hours
This instructor-led, live training in Swatar (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.
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Advanced Machine Learning Models with Google Colab

 21 Hours
This instructor-led, live training in Swatar (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.
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Creating Custom Chatbots with Google AutoML

 14 Hours
This instructor-led, live training in Swatar (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.
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Optimizing AI Models for Edge Devices

 14 Hours
This instructor-led, live training in Swatar (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.
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Building AI Solutions on the Edge

 14 Hours
This instructor-led, live training in Swatar (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.
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Edge AI with TensorFlow Lite

 14 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications. By the end of this training, participants will be able to:
  • Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
  • Develop and optimize AI models using TensorFlow Lite.
  • Deploy TensorFlow Lite models on various edge devices.
  • Utilize tools and techniques for model conversion and optimization.
  • Implement practical Edge AI applications using TensorFlow Lite.
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AI-Powered Autonomous Systems

 21 Hours
This instructor-led, live training in Swatar (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.
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AI-Enhanced Yield Management in Semiconductor Production

 14 Hours
This instructor-led, live training in Swatar (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.
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Advanced AI Techniques for Semiconductor Design Automation

 21 Hours
This instructor-led, live training in Swatar (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.
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AI-Driven Process Optimization in Chip Fabrication

 14 Hours
This instructor-led, live training in Swatar (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.
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Apache Airflow for Data Science: Automating Machine Learning Pipelines

 21 Hours
This instructor-led, live training in Swatar (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.
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Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

 21 Hours
This instructor-led, live training in Swatar (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
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AI-Powered Cybersecurity: Advanced Threat Detection & Response

 28 Hours
This instructor-led, live training in Swatar (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.
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Machine Learning with Google Colab

 14 Hours
This instructor-led, live training in Swatar (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.
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AI-Powered Cybersecurity: Threat Detection & Response

 21 Hours
This instructor-led, live training in Swatar (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.
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Data Mining with Weka

 14 Hours
This instructor-led, live training in Swatar (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.
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Advanced Analytics with RapidMiner

 14 Hours
This instructor-led, live training in Swatar (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.
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Introduction to Stable Diffusion for Text-to-Image Generation

 21 Hours
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.
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Machine Learning with Python – 2 Days

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning with Python – 4 Days

 28 Hours
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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AdaBoost Python for Machine Learning

 14 Hours
This instructor-led, live training in Swatar (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.
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AutoML with Auto-Keras

 14 Hours
This instructor-led, live training in Swatar (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.
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AutoML

 14 Hours
This instructor-led, live training in Swatar (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.
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Machine Learning

 21 Hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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Deep Learning Neural Networks with Chainer

 14 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start developing neural network models.
  • Define and implement neural network models using a comprehensible source code.
  • Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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Pattern Recognition

 21 Hours
This instructor-led, live training in Swatar (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.
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DataRobot

 7 Hours
This instructor-led, live training in Swatar (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.
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Feature Engineering for Machine Learning

 14 Hours
This instructor-led, live training in Swatar (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.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Google Cloud AutoML

 7 Hours
This instructor-led, live training in Swatar (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.
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Machine Learning Algorithms in Julia

 21 Hours
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.
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Kubeflow

 35 Hours
This instructor-led, live training in Swatar (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.
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Kubeflow Fundamentals

 28 Hours
This instructor-led, live training in Swatar (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.
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Kubeflow on AWS

 28 Hours
This instructor-led, live training in Swatar (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.
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Kubeflow on Azure

 28 Hours
This instructor-led, live training in Swatar (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.
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Mathematica for Machine Learning

 14 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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Machine Learning for Banking (with Python)

 21 Hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning and Big Data

 7 Hours
This instructor-led, live training in Swatar (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.
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Machine Learning Concepts for Entrepreneurs and Managers

 21 Hours
This training course is for people that would like to apply Machine Learning in practical applications for their team.  The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. Business Analysts & Investors
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Machine Learning for Finance (with Python)

 21 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to:
  • Understand the fundamental concepts in machine learning
  • Learn the applications and uses of machine learning in finance
  • Develop their own algorithmic trading strategy using machine learning with Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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MLflow

 21 Hours
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.
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Machine Learning Fundamentals with Scala and Apache Spark

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Introduction to Machine Learning

 7 Hours
This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.
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Machine Learning on iOS

 14 Hours
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to:
  • Create a mobile app capable of image processing, text analysis and speech recognition
  • Access pre-trained ML models for integration into iOS apps
  • Create a custom ML model
  • Add Siri Voice support to iOS apps
  • Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
  • Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Mobile Apps using Google’s ML Kit

 14 Hours
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.
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Machine Learning in business – AI/Robotics

 14 Hours
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
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Machine Learning for Robotics

 21 Hours
This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
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Pattern Matching

 14 Hours
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Format of the Course
  • This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
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Machine Learning with Random Forest

 14 Hours
This instructor-led, live training in Swatar (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.
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RapidMiner for Machine Learning and Predictive Analytics

 14 Hours
RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.  In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.  By the end of this training, participants will be able to:
  • Install and configure RapidMiner
  • Prepare and visualize data with RapidMiner
  • Validate machine learning models
  • Mashup data and create predictive models
  • Operationalize predictive analytics within a business process
  • Troubleshoot and optimize RapidMiner
Audience
  • Data scientists
  • Engineers
  • Developers
Format of the Course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Tensorflow Lite for Microcontrollers

 21 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices. By the end of this training, participants will be able to:
  • Install TensorFlow Lite.
  • Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
  • Add AI to hardware devices without relying on network connectivity.
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Testimonials (24)

Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.

Rick Johnson - Laramie County Community College

Course - Artificial Intelligence (AI) Overview

the VM is a nice idea

Vincent - REGNOLOGY ROMANIA S.R.L.

Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning

I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.

Jenna - TCMT

Course - Machine Learning with Python – 2 Days

The clarity with which it was presented

John McLemore - Motorola Solutions

Course - Deep Learning for Telecom (with Python)

the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose

Guillaume GAUTIER - OLEA MEDICAL

Course - MLflow

The way of transferring knowledge and the knowledge of the trainer.

Jakub Rekas - Bitcomp Sp. z o.o.

Course - Machine Learning on iOS

The explaination

Wei Yang Teo - Ministry of Defence, Singapore

Course - Machine Learning with Python – 4 Days

I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM

Course - Kubeflow

The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.

Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH

Course - Machine Learning Concepts for Entrepreneurs and Managers

The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.

Robert Baker

Course - Deep Learning with TensorFlow 2.0

Convolution filter

Francesco Ferrara

Course - Introduction to Machine Learning

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

Course - TensorFlow Extended (TFX)

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course - Natural Language Processing with TensorFlow

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

Course - Applied AI from Scratch in Python

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

Course - Machine Learning

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

Course - Python for Advanced Machine Learning

Very flexible.

Frank Ueltzhoffer

Course - Artificial Neural Networks, Machine Learning and Deep Thinking

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course - TensorFlow for Image Recognition

I liked the new insights in deep machine learning.

Josip Arneric

Course - Neural Network in R

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.

Sebastiaan Holman

Course - Machine Learning and Deep Learning

The global overview of deep learning.

Bruno Charbonnier

Course - Advanced Deep Learning

The topic is very interesting.

Wojciech Baranowski

Course - Introduction to Deep Learning

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course - Introduction to the use of neural networks

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Provisional Upcoming Courses (Contact Us For More Information)

Machine Learning with Google Colab

2025-06-17 09:30
14 hours
Swatar, City Centre

Machine Learning with Python – 2 Days

2025-07-01 09:30
14 hours
Swatar, City Centre

Machine Learning on iOS

2025-07-15 09:30
14 hours
Swatar, City Centre

Google Cloud AutoML

2025-07-29 09:30
7 hours
Swatar, City Centre

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