Available online or on-site, these instructor-led live Machine Learning (ML) training courses provide hands-on practice to demonstrate how to apply ML techniques and tools for solving real-world challenges across various industries. NobleProg ML curricula encompass a range of programming languages and frameworks, including Python, R, and Matlab. The courses are tailored for numerous industry applications, such as Finance, Banking, and Insurance, and cover everything from Machine Learning fundamentals to advanced methodologies like Deep Learning.
Machine Learning training can be delivered as either “online live training” or “onsite live training.” Online live training (also known as “remote live training”) is conducted via an interactive remote desktop session. Onsite live training is available locally at customer premises in Athens or within NobleProg corporate training centers in Athens.
Take Metro Line 3 (M3) from Airport toward Dimotiko Theatro.
Get off at Evangelismos.
The address is on Vasilissis Sofias Avenue, very close to the station. Evangelismos station is directly on the avenue.
Allow roughly 40–50 minutes total, depending on waiting/walking time.
From Athens Central Railway Station (Larissa Station)
Take the metro to Syntagma and change to Line 3 toward Doukissis Plakentias/Airport.
Get off at Evangelismos.
Walk to No. 46.
By bus
There are several buses running along Vasilissis Sofias Avenue. Nearby stops include Rigilis Street and Syntagma, while the Evangelismos area is served by multiple bus routes.
The metro is generally the simplest option, especially if you're arriving with luggage.
This instructor-led, live training in Athens (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 Athens (online or onsite) is designed for participants with diverse backgrounds who want to utilize Google's AutoML platform to develop customized chatbots for various 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 Athens (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 live instructor-led training in Athens, available online or onsite, targets intermediate developers, data scientists, and tech enthusiasts seeking to develop practical competencies in deploying AI models on edge devices for diverse applications.
By the conclusion of the program, participants will be able to:
Understand the fundamental principles of Edge AI and its advantages.
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 models deployed on the edge.
Address ethical and security considerations in Edge AI applications.
This instructor-led, live training in Athens (online or onsite) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience. The course aims to help participants enhance DeepSeek model performance, minimize latency, and efficiently deploy AI solutions using modern MLOps practices.
Upon completion 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 on Athens empowers intermediate practitioners to develop automated MLOps pipelines on Kubernetes. Participants will learn to design CI/CD workflows, apply GitOps strategies, and deploy ML models using containerized infrastructure, enabling scalable and reproducible machine learning operations.
This practical training in Athens provides the skills necessary to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, design scalable pipelines, and manage production-ready workloads by adhering to best practices.
This instructor-led training in Athens empowers advanced professionals with the competencies to design, optimize, and deploy complete TinyML pipelines. Through practical labs, participants will master data collection, low-power model training, and real-world application testing.
This instructor-led live training in Athens (online or onsite) is tailored for intermediate-level developers, data scientists, and AI practitioners looking to leverage TensorFlow Lite for Edge AI applications.
By the conclusion 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 Athens (online or in-person) is designed for senior professionals aiming to gain mastery over the technologies underpinning autonomous systems.
Upon completion of this training, participants will be capable of:
Designing and deploying AI models for autonomous decision-making processes.
Creating control algorithms for autonomous navigation and obstacle avoidance.
Ensuring safety and reliability within AI-driven autonomous systems.
Integrating autonomous systems with existing robotics and AI frameworks.
This live, instructor-led training on Athens (delivered online or onsite) is tailored for advanced professionals looking to strengthen their grasp of computer vision and investigate TensorFlow’s potential for creating complex vision models using Google Colab.
Upon completion of this training, participants will be able to:
Develop 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 boost the performance of CNN models.
Visualize and interpret the results of image classification models.
This instructor-led training in Athens empowers advanced professionals to safeguard TinyML pipelines on edge devices. You will gain the skills to deploy privacy-preserving techniques, reinforce models against adversarial threats, and implement best practices for secure data handling in constrained environments.
This live, instructor-led training in Athens (online or onsite) is designed for advanced professionals seeking to deepen their knowledge of machine learning models, improve their hyperparameter tuning skills, 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 session (online or on-site) is designed for intermediate-level professionals who want 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 Athens (online or onsite) targets 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 Athens (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 Greece (online or on-site) is designed for advanced professionals aiming to apply cutting-edge AI techniques to semiconductor design automation, thereby enhancing efficiency, accuracy, and innovation in chip design and verification.
Upon completion of this training, participants will be capable of:
Utilizing advanced AI techniques to streamline semiconductor design workflows.
Embedding machine learning models into EDA tools to strengthen design verification processes.
Creating AI-powered solutions to address complex challenges in chip fabrication.
Employing neural networks to enhance the speed and accuracy of design automation.
This instructor-led, live training in Athens (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 Athens (online or onsite) is designed for intermediate professionals who want to understand and apply AI techniques to optimize semiconductor fabrication processes.
Upon completing this training, participants will be equipped to:
Grasp AI methodologies for optimizing processes in chip fabrication.
Deploy AI models to improve yield and minimize defects.
Evaluate process data to pinpoint critical parameters for optimization.
Utilize machine learning techniques to fine-tune semiconductor production processes.
This instructor-led, live training in Athens (online or onsite) is designed for 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 Athens (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 live, instructor-led training in Athens empowers advanced practitioners to refine TinyML models for use on resource-limited embedded devices. Learners will utilize quantization and pruning methods, construct low-latency inference pipelines, and assess performance within specific memory and energy boundaries.
This instructor-led, live training in Athens (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 session in Athens (available online or onsite) targets beginner-level professionals seeking to understand and apply AI technologies in the semiconductor manufacturing sector.
By the conclusion of this training, participants will be able to:
Understand the fundamental principles of AI and their relevance to semiconductor manufacturing.
Identify key areas in semiconductor manufacturing suitable for effective AI implementation.
Utilize AI tools and techniques to enhance production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led course in Athens walks professionals through the process of containerizing entire ML pipelines with Docker. Participants will gain mastery in creating reproducible environments, orchestrating training and inference tasks, and establishing CI/CD workflows for scalable MLOps deployments.
This instructor-led live training in Athens (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 Athens (online or onsite) targets intermediate-level data professionals aiming 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 Athens (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts who aim 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 (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 Athens (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
Upon completing this training, participants will be capable of:
Grasping the core principles of TinyML and its advantages for edge AI applications.
Configuring a development environment tailored for TinyML projects.
Training, optimizing, and deploying AI models on low-power microcontrollers.
Utilizing TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
Enhancing AI models for improved power efficiency and adherence to memory limitations.
This instructor-led, live training in Athens (online or onsite) is designed for beginner-level cybersecurity professionals eager to learn how to utilize AI for enhanced threat detection and response capabilities.
Upon completion of this training, participants will be able to:
Comprehend AI applications within cybersecurity.
Deploy AI algorithms for identifying threats.
Automate incident response using AI tools.
Incorporate AI into current cybersecurity infrastructure.
This instructor-led live training, offered in Athens (via online or onsite delivery), is designed for biologists who wish to understand the mechanisms of AlphaFold and apply its models to guide their experimental studies.
By the conclusion of this training, participants will be able to:
Understand the core principles of AlphaFold.
Learn how AlphaFold operates.
Learn how to interpret AlphaFold predictions and results.
This instructor-led live training in Athens (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.
Build practical expertise in applying Machine Learning techniques with Python at this Athens training. The curriculum covers core algorithms, including regression, classification, and clustering, guiding you through making modeling decisions, interpreting outputs, and validating results through real-world case studies.
This practical training in Athens enables programmers to develop AI models from scratch using Python. You will gain expertise in supervised learning, neural networks, and unsupervised techniques through the use of scikit-learn and Apache Spark. The course emphasizes hands-on Jupyter exercises designed to solve real-world problems.
This instructor-led training on Athens delves into the theoretical basis and practical application of Deep Reinforcement Learning using Python. Learners will construct and train DRL agents with TensorFlow or PyTorch, leveraging algorithms such as DQN and PPO to address complex real-world challenges.
A foundational training module in Athens that covers the basics of AI, ranging from intelligent agents to machine learning. It empowers executives and architects to evaluate new AI trends, integrate actionable solutions, and boost business agility through automated strategies.
Discover how Machine Learning and Deep Learning are reshaping the automotive sector. This Athens course delves into fundamental concepts, progressing from simple automation to fully autonomous decision-making. It also covers neural networks and demonstrates practical TensorFlow implementations for real-world applications.
This 8-day programme takes participants on a comprehensive journey, building robust Python engineering skills that lead directly into advanced AI system design. Attendees will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production environments. The curriculum prioritises reliability, evaluation, safety, and real-world deployment over mere experimentation.
This three-day program in Athens delves into both the theoretical underpinnings and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Attendees will examine various network architectures, learning mechanisms, and the mathematical principles involved, progressing from foundational perceptrons to sophisticated deep learning methodologies.
Develop expertise in Machine Learning algorithms like Naive Bayes, Decision Trees, Neural Networks, SVMs, and Clustering through this practical Athens course. Acquire hands-on skills in model evaluation, bias-variance trade-offs, and deep learning to design robust predictive solutions.
This instructor-led, live training in Athens (online or onsite) provides an introduction to the fields of pattern recognition and machine learning. It covers 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 Athens (online or onsite) targets data scientists who wish to utilize TensorFlow for analyzing potential fraud data.
By the conclusion of this training, participants will be able to:
Create a fraud detection model using Python and TensorFlow.
Build linear regressions and models to predict fraud.
Develop an end-to-end AI application for analyzing fraud data.
This instructor-led live training program on Athens delves into the fundamentals of AI, machine learning, and deep learning. Participants will leverage Python, Keras, and TensorFlow to develop practical telecom models, including those for credit risk and churn prediction, thereby acquiring hands-on skills essential for real-world data science applications.
This practical, instructor-led session serves as a logical progression from the Python for Data Analysis course.
Participants will be introduced to fundamental Machine Learning concepts and learn how to apply them directly to data analysis challenges, including prediction, classification, and segmentation.
The emphasis is on practical implementation using widely known tools such as Python, Pandas, and Jupyter Notebook, allowing learners to grasp Machine Learning concepts without needing an advanced mathematical background.
This live, instructor-led training in Athens (delivered online or onsite) is designed for developers and data scientists aiming to build, deploy, and administer machine learning workflows on Kubernetes.
By the conclusion of this program, participants will be equipped to:
Install and configure Kubeflow in on-premise and cloud settings.
Construct, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
Operate full machine learning pipelines across diverse architectures and cloud environments.
Utilize Kubeflow to launch and manage Jupyter notebooks.
Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led live training in Athens (online or on-site) is designed for engineers who wish to evaluate available approaches and tools to make informed decisions regarding the adoption of MLOps within their organizations.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the appropriate team with the necessary 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 reproducible and maintainable.
This instructor-led, live training in Athens (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists looking 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 Athens, offered online or onsite, targets developers and data scientists looking to utilize TensorFlow 2.x for building predictors, classifiers, generative models, and neural networks.
By the end of the program, participants will be capable of:
Installing and configuring TensorFlow 2.x.
Understanding the benefits of TensorFlow 2.x over prior versions.
Building deep learning models.
Implementing an advanced image classifier.
Deploying deep learning models to cloud, mobile, and IoT devices.
This 35-hour course in Athens explores the fundamentals of deep neural networks, including CNNs, RNNs, and generative models such as GANs. Attendees gain practical experience with Theano and TensorFlow, learning to construct, train, and deploy production-grade deep learning models for real-world scenarios.
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
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