Online or onsite, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Leuven onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Leuven
Park Inn by Radisson Leuven, Martelarenlaan 36, Leuven, Belgium, 3010
Leuven
Louvain (in Dutch Leuven, in German Löwen) is a Dutch-speaking city in Belgium located in the Flemish Region, capital of the province of Flemish Brabant and capital of the district that bears its name. It is watered by the Dyle, a tributary of the Rupel. It is a university city where the Katholieke Universiteit Leuven is located, a Dutch-speaking branch born from the split of the oldest university in Belgium. Leuven is also known for hosting the headquarters of AB InBev, the largest brewery in the world. Leuven is the beer capital of Belgium.
This instructor-led, live training in Leuven (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 Leuven (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 Leuven (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 Leuven (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 Leuven (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.
This live training in Leuven helps intermediate practitioners build automated MLOps pipelines on Kubernetes. Participants design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure for scalable, reproducible machine learning operations.
This hands-on training in Leuven equips you with the skills to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads with best practices.
This instructor-led training in Leuven equips advanced professionals with the skills to design, optimize, and deploy complete TinyML pipelines. Participants will learn to collect data, train low-power models, and test real-world applications using practical labs.
This instructor-led, live training in Leuven (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.
This instructor-led, live training in Leuven (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 Leuven (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 training in Leuven equips advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.
This instructor-led, live training in Leuven (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 Leuven (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 Leuven (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 Leuven (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
By the end of this training, participants will be able to:
Implement AI models for predicting 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 Leuven (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 Leuven (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end 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 Leuven (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 Leuven (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 Leuven (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 Leuven equips advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, implement low-latency inference pipelines, and benchmark performance against memory and energy limits.
This instructor-led, live training in Leuven (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.
By the end of this training, participants will be able to:
Understand the challenges of explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This instructor-led, live training in Leuven (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.
By the end of this training, participants will be able to:
Understand the basic principles of AI and how they apply to semiconductor manufacturing.
Identify areas within semiconductor manufacturing where AI can be effectively implemented.
Utilize AI tools and techniques to enhance production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led training in Leuven guides professionals through containerizing complete ML pipelines using Docker. Participants will master building reproducible environments, orchestrating training and inference workloads, and implementing CI/CD for scalable MLOps deployments.
This instructor-led, live training in Leuven (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 Leuven (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 Leuven (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 Leuven (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 Leuven (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.
This instructor-led, live training in Leuven (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 Leuven (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 Leuven (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.
Develop practical proficiency in applying Machine Learning methods with Python at this Leuven training. This course covers core algorithms like regression, classification, and clustering, teaching you to make modeling decisions, interpret outputs, and validate results using real-world examples.
This practical training in Leuven helps programmers build AI models from scratch using Python. You will master supervised learning, neural networks, and unsupervised techniques using scikit-learn and Apache Spark. It focuses on hands-on Jupyter exercises for real-world problem solving.
This instructor-led training in Leuven covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
An introductory training in Leuven exploring AI fundamentals, from intelligent agents to machine learning. It equips executives and architects to assess emerging AI trends, integrate practical solutions, and accelerate business agility through automated strategies.
Explore how Machine Learning and Deep Learning transform the automotive sector. This Leuven course covers core concepts from simple automation to autonomous decision-making, including neural networks and practical TensorFlow examples for real-world applications.
This 8-day programme provides a complete journey from strong Python engineering foundations to advanced AI system design. Participants develop disciplined coding practices, master statistical and deep learning methods and build production-ready generative AI and agent-based systems. The focus is on reliability, evaluation, safety and real-world deployment rather than experimentation alone.
This three-day program in Leuven covers the theory and practice of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will explore network architectures, learning mechanisms, and mathematical foundations, moving from basic perceptrons to advanced deep learning techniques.
Master Machine Learning algorithms including Naive Bayes, Decision Trees, Neural Networks, SVMs, and Clustering in this hands-on Leuven course. Build practical skills in model evaluation, bias-variance trade-offs, and deep learning to create robust predictive solutions.
This instructor-led, live training in Leuven (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 Leuven (online or onsite) is aimed at data scientists who wish 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.
This instructor-led live training in Leuven covers AI, machine learning, and deep learning fundamentals. Participants will use Python, Keras, and TensorFlow to build practical telecom models, including credit risk and churn prediction, gaining hands-on skills for real-world data science applications.
This practical, instructor-led training is designed as a natural continuation of the Python for Data Analysis course.
It introduces participants to the core concepts of Machine Learning and shows how these can be applied directly to data analysis tasks such as prediction, classification, and segmentation.
The focus is on understanding how Machine Learning works in practice, using familiar tools such as Python, Pandas, and Jupyter Notebook, without requiring an advanced mathematical background.
This instructor-led, live training in Leuven (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 Leuven (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
Prepare, validate and version data for use by ML models.
Understand the components of an ML Pipeline and the tools needed to build one.
Experiment with different machine learning frameworks and servers for deploying to production.
Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
This instructor-led, live training in Leuven (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.
This instructor-led, live training in Leuven (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
Install and configure TensorFlow 2.x.
Understand the benefits of TensorFlow 2.x over previous versions.
Build deep learning models.
Implement an advanced image classifier.
Deploy a deep learning model to the cloud, mobile and IoT devices.
This 35-hour course in Leuven covers deep neural network fundamentals, including CNNs, RNNs, and generative models like GANs. Participants gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
The training provided an interesting overview of deep learning models and related methods. The topic was quite new to me, but now I feel like I actually have an idea of what AI and ML can involve, what these terms consist of and how they can be used advantageously. In general, I liked the approach of starting with the statistical background and the basic learning models, such as linear regression, especially emphasizing the exercises in between.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course - Machine Learning – Data science
Provisional Upcoming Courses (Contact Us For More Information)
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