Online Machine Learning Training in Norway

This instructor-led, live training in Norway (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 Norway (online or onsite) is designed for participants with varying levels of expertise who wish to utilize Google's AutoML platform to construct customized chatbots for a range of applications.

Upon completion of this training, participants will be able to:

• Grasp 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 Norway (online or onsite) is designed for AI developers, machine learning engineers, and system architects at an intermediate level who aim to optimize AI models for edge deployment.

Upon completion of this training, participants will be able to:

• Grasp the challenges and prerequisites for deploying AI models on edge devices.
• Apply model compression techniques to minimize the size and complexity of AI models.
• Leverage quantization methods to boost model efficiency on edge hardware.
• Implement pruning and other optimization strategies to enhance model performance.
• Deploy optimized AI models across various edge devices.

This live, instructor-led training in Norway (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts looking to gain practical skills in deploying AI models on edge devices for various applications.

Upon completion of this training, participants will be able to:

• Grasp the core principles of Edge AI and its advantages.
• Establish and configure the necessary edge computing infrastructure.
• Create, train, and refine AI models tailored for edge deployment.
• Deploy practical AI solutions onto edge hardware.
• Assess and enhance the performance of models running on the edge.
• Navigate ethical and security aspects inherent to Edge AI applications.

This instructor-led, live training in Norway (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

This live, instructor-led training, conducted in Norway (either online or onsite), is designed for intermediate-level developers, data scientists, and AI professionals seeking to apply TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be capable of:

• Comprehending the basics of TensorFlow Lite and its function in Edge AI.
• Developing and optimizing AI models via TensorFlow Lite.
• Deploying TensorFlow Lite models onto various edge devices.
• Applying tools and methods for model conversion and optimization.
• Implementing functional Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals seeking to master the technologies underpinning autonomous systems.

Upon completion of this training, participants will be equipped to:

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

This instructor-led, live training in Norway (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led live training in Norway (online or onsite) is designed for advanced professionals seeking to deepen their expertise in machine learning models, sharpen their hyperparameter tuning skills, and learn effective model deployment techniques 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 Norway (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 Norway (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 guided, live training in Norway (online or onsite) targets intermediate professionals seeking to apply AI-based predictive maintenance strategies in semiconductor manufacturing to improve production efficiency and minimize unexpected equipment failures.

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

• Implement AI models for forecasting equipment failures in semiconductor manufacturing.
• Analyze maintenance data to identify patterns and trends indicative of potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals aiming to apply cutting-edge AI techniques to semiconductor design automation, thereby improving efficiency, accuracy, and innovation in chip design and verification.

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

• Apply advanced AI methods 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 live, instructor-led training in Norway (online or onsite) targets intermediate-level data scientists and developers eager to understand and apply deep learning techniques within the Google Colab ecosystem.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

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

Upon completion of this training, participants will be able to:

• Comprehend AI methodologies for optimizing processes in chip fabrication.
• Deploy AI models to increase yield and minimize defects.
• Analyze process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led live training in Norway (online or onsite) is designed for intermediate-level participants who want 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 orchestrating machine learning workflows.
• 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 Norway (online or onsite) targets intermediate-level data scientists and developers looking to efficiently apply machine learning algorithms within the Google Colab environment.

Upon completing this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Utilize libraries such as Scikit-learn for data analysis and prediction.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals eager to investigate cutting-edge XAI techniques for deep learning models, with a strong emphasis on developing interpretable AI systems.

Upon completion of this training, participants will be able to:

• Grasp the challenges associated with explainability in deep learning.
• Deploy advanced XAI techniques for neural networks.
• Analyze decisions generated by deep learning models.
• Assess the trade-offs between performance and transparency.

This instructor-led, live training in Norway (online or onsite) is designed for beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.

Upon completing this training, participants will be able to:

• Grasp the fundamental principles of AI and their application to semiconductor manufacturing.
• Recognize specific areas in semiconductor manufacturing where AI can be successfully deployed.
• Leverage AI tools and techniques to improve production efficiency and quality control.
• Deploy basic AI models to optimize manufacturing workflows.

Docker serves as a containerization platform designed to construct reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals aiming to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines leveraging Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures reinforced by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab sessions for implementing end-to-end containerized workflows.

Course Customization Options

• For customized training tailored to specific ML infrastructure requirements, please contact us to explore available options.

This instructor-led, live training in Norway (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 Norway (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:

• Grasp the fundamental concepts and types of machine learning.
• Apply essential algorithms for classification, regression, clustering, and association analysis.
• Conduct exploratory data analysis and prepare data using Python.
• Utilize neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Assess and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Norway (online or onsite) is designed for intermediate to advanced 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:

• Comprehend advanced deep learning architectures and methodologies for text-to-image generation.
• Deploy complex models and optimization strategies to achieve high-fidelity image synthesis.
• Enhance performance and scalability when working with large datasets and intricate models.
• Refine hyperparameters to improve model performance and generalization capabilities.
• Seamlessly integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Norway (online or onsite) is designed for intermediate to advanced cybersecurity professionals who wish to enhance their skills in AI-driven threat detection and incident response.

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

• Deploy advanced AI algorithms for real-time threat detection.
• Customize AI models to address specific cybersecurity challenges.
• Develop automation workflows for efficient threat response.
• Protect AI-driven security tools against adversarial attacks.

This instructor-led, live training in Norway (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 Norway (online or on-site) 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 Norway (online or onsite) is tailored for beginner to intermediate-level developers and data scientists who want to learn the essentials of LightGBM and explore advanced techniques.

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

• Install and configure LightGBM.
• Understand the theory behind gradient boosting and decision tree algorithms.
• Use LightGBM for basic and advanced machine learning tasks.
• Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
• Integrate LightGBM with other machine learning frameworks.
• Troubleshoot common issues in LightGBM.

This instructor-led live training in Norway (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 (available online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers looking to leverage Stable Diffusion for generating high-quality images across various use cases.

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

• Understand the principles of Stable Diffusion and its operational logic 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 practical proficiency in applying Machine Learning techniques. Utilizing the Python programming language and its extensive ecosystem of libraries, alongside numerous hands-on examples, the curriculum covers the essential components of Machine Learning. It guides learners through making informed data modeling decisions, interpreting algorithm outputs, and validating results effectively.

Our objective is to provide you with the confidence to master the fundamental tools of the Machine Learning toolbox, while helping you steer clear of common pitfalls encountered in Data Science applications.

The course Applied AI from Scratch in Python empowers programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers core principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. The curriculum examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess algorithmic limitations, and complete applied projects designed for solving real-world problems.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives many contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions through trial-and-error mechanisms based on reward signals.

This instructor-led live training is available both online and onsite. It is designed for intermediate-level developers and data scientists who want to master and apply Deep Reinforcement Learning techniques to create intelligent agents capable of making autonomous decisions in complex settings.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Implement essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications like gaming, robotics, and decision optimization.
• Use modern tools to troubleshoot, visualize, and optimize training performance.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, using PyTorch instead of TensorFlow), please contact us to make arrangements.

Delving into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course examines essential concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics such as communication, perception, and autonomous action. It empowers executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technological trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in Norway (available online or on-site) is designed for data scientists and software engineers who wish to employ AdaBoost to create boosting algorithms for machine learning using Python.

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

• Establish the necessary development environment to initiate the creation of machine learning models with AdaBoost.
• Comprehend the ensemble learning approach and how to execute adaptive boosting.
• Learn the process of constructing AdaBoost models to enhance machine learning algorithms in Python.
• Utilize hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive sector. It guides participants in identifying suitable technologies for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making systems.

Spanning 8 days, this programme takes participants on a comprehensive journey from robust Python engineering basics to the design of sophisticated AI systems. Learners will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production. The curriculum emphasises reliability, evaluation, safety, and practical deployment, moving beyond mere experimentation.

An Artificial Neural Network is a computational data model employed in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely utilized in Machine Learning (ML) applications, which serve as a specific implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

Enhance your data science proficiency with this in-depth Machine Learning training program, which explores essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Benefit from practical experience grounded in theoretical principles and applied through real-world scenarios. This course is particularly suitable for data analysts, software engineers, AI enthusiasts, and business leaders aiming to implement machine learning solutions. Gain mastery over classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to develop robust predictive models.

This instructor-led live training in Norway (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code is 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 comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Norway (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 Norway (online or on-site) targets data scientists who intend to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Machine learning represents a domain within Artificial Intelligence that enables computers to learn from data without being explicitly programmed. <\/p>

Deep learning is a specialized subfield of machine learning that employs techniques based on learning data representations and structures, such as neural networks. <\/p>

Python is a high-level programming language renowned for its clear syntax and high code readability. <\/p>

During this instructor-led live training, participants will learn how to implement deep learning models for the telecommunications sector using Python, guided by the step-by-creation of a deep learning credit risk model. <\/p>

Upon completion of this training, participants will be capable of: <\/p>

• Grasping the fundamental concepts of deep learning. <\/li>
• Understanding the applications and utility of deep learning in the telecom industry. <\/li>
• Utilizing Python, Keras, and TensorFlow to develop deep learning models for telecom. <\/li>
• Constructing their own deep learning model for predicting customer churn using Python. <\/li> <\/ul>

  Course Format<\/strong> <\/p>

  • Interactive lectures and discussions. <\/li>
  • Extensive exercises and practice sessions. <\/li>
  • Hands-on implementation in a live-lab environment. <\/li> <\/ul>

    Course Customization Options<\/strong> <\/p>

    • To request customized training for this course, please contact us to arrange. <\/li> <\/ul>