Machine Learning Training in Nantes

This instructor-led, live training in Nantes (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 Nantes (online or onsite) targets participants with varying levels of expertise who aim to utilize Google's AutoML platform to develop customized chatbots for diverse 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 assess 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 Nantes (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, conducted in Nantes (online or onsite), targets intermediate-level developers, data scientists, and technology enthusiasts aiming to build practical skills in deploying AI models on edge devices for diverse applications.

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

• Understand the core principles of Edge AI and its associated benefits.
• Establish and configure the necessary edge computing environment.
• Create, train, and optimize AI models for deployment on edge systems.
• Implement practical AI solutions using edge hardware.
• Assess and improve the performance of models running on edge devices.
• Navigate ethical and security considerations inherent in Edge AI applications.

This instructor-led, live training in Nantes (online or onsite) targets advanced AI engineers and data scientists with intermediate-to-advanced expertise who aim to boost DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

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

• Optimizing DeepSeek models for efficiency, accuracy, and scalability.
• Applying best practices for MLOps and model versioning.
• Deploying DeepSeek models across cloud and on-premise infrastructure.
• Effectively monitoring, maintaining, and scaling AI solutions.

This instructor-led training in Nantes empowers advanced professionals to design, optimize, and deploy complete TinyML pipelines. Through practical labs, participants will learn to gather data, train low-power models, and validate real-world applications.

This instructor-led, live training in Nantes (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 Nantes (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 Nantes (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 session in Nantes empowers advanced professionals to secure TinyML workflows on edge hardware. You will master the implementation of privacy-centric methods, reinforcement of models against adversarial threats, and the application of best practices for secure data processing in resource-limited environments.

This instructor-led, live training in Nantes (online or onsite) is designed for advanced-level professionals who want to expand their knowledge of machine learning models, improve their hyperparameter tuning skills, and learn how to effectively deploy models 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 Nantes (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 Nantes (online or onsite) targets intermediate-level business and AI professionals seeking to implement machine learning in business contexts, forecasting, and AI-driven systems through real case studies and Python-based tools.

Upon completing this training, participants will be able to:

• Comprehend the role of machine learning within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling purposes.
• Utilize neural networks for classification and prediction tasks.
• Execute 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, held Nantes (online or onsite), is designed for intermediate professionals aiming to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve 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 Nantes (available online or onsite) is designed for advanced professionals who aim to apply cutting-edge AI techniques to semiconductor design automation, thereby 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 Nantes (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 Nantes (online or on-site) targets intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this training, participants will be able to:

• Grasp AI methodologies used for process optimization in chip fabrication.
• Deploy AI models to improve yield and minimize defects.
• Examine process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led live training, held in Nantes (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 Nantes (online or onsite) is designed for 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 Nantes empowers advanced practitioners to refine TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, build low-latency inference pipelines, and evaluate performance relative to memory and energy limits.

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

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

• Grasp the fundamental principles of AI and their application in semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively implemented.
• Use AI tools and techniques to enhance production efficiency and quality control.
• Implement basic AI models to optimize manufacturing processes.

This instructor-led, live training in Nantes (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 guided, live training in Nantes (online or on-site) is designed for intermediate data professionals seeking to apply machine learning techniques to business issues, including sales forecasting and predictive modeling with neural networks.

By the end of this program, 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 Nantes (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking to expand their expertise 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 Nantes (online or onsite) is designed for intermediate to advanced cybersecurity professionals seeking to enhance their skills in AI-driven threat detection and incident response.

Upon completion 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.
• Create automation workflows for threat response.
• Protect AI-driven security tools from adversarial attacks.

This instructor-led, live training in Nantes (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

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

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

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

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

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

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

This eight-day programme guides participants through a comprehensive journey, starting from solid Python engineering principles and progressing to advanced AI system architecture. Learners cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct production-ready generative AI and agent-based solutions. The curriculum prioritises reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

This instructor-led, live training in Nantes (online or on-site) 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 Nantes (online or onsite) targets data scientists who want 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 hands-on, instructor-led training serves as a logical follow-up to the Python for Data Analysis course.

Participants are introduced to the fundamental concepts of Machine Learning and learn how to apply them directly to data analysis tasks, including prediction, classification, and segmentation.

The course emphasizes practical understanding, utilizing familiar tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced mathematical background.

This guided live training in Nantes, offered online or on-site, is designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of the session, participants will be equipped to:

• Install and configure Kubeflow in on-premise and cloud environments.
• Construct, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Execute full machine learning pipelines across diverse architectures and cloud ecosystems.
• Utilize Kubeflow to launch and manage Jupyter notebooks.
• Create ML training, hyperparameter tuning, and serving workloads across various platforms.

This instructor-led, live training in Nantes (online or onsite) is designed for engineers who wish to evaluate the current approaches and tools available, helping them make informed decisions about the future adoption of MLOps within their organizations.

Upon completing 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 reproducible and maintainable.

This instructor-led, live training in Nantes (online or onsite) targets 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 Nantes (online or onsite) is designed for developers and data scientists who intend to utilize Tensorflow 2.x for building predictors, classifiers, generative models, and neural networks, among other applications.

Upon completion of this training, participants will be capable of:

• Installing and configuring TensorFlow 2.x.
• Understanding the advantages of TensorFlow 2.x over earlier versions.
• Developing deep learning models.
• Implementing an advanced image classifier.
• Deploying deep learning models to the cloud, mobile applications, and IoT devices.