Machine Learning Training in Lyon

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

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

• Grasping the principles of Edge AI and its associated advantages.
• Establishing and configuring the edge computing environment.
• Creating, training, and optimizing AI models for edge deployment.
• Implementing functional AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed at the edge.
• Tackling ethical and security issues inherent in Edge AI applications.

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:

• Explore the Kubeflow ecosystem and its core components.
• Develop reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs within a Kubernetes environment.
• Deploy machine learning models efficiently via Kubeflow Serving.

Format of the Course also allows for the evaluation of participants.

• 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 instructor-led, live training in Lyon (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 Lyon (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 Lyon (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 Lyon (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 in Lyon (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 Lyon (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 Lyon (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 Lyon (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 instructor-led, live training in Lyon (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 Lyon (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 Lyon (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 Lyon (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 Lyon (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 (available online or onsite) is designed for technical professionals with a background in machine learning who want to optimize models for identifying complex patterns in big data using AutoML frameworks.

This instructor-led, live training in Lyon (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 course aims to build foundational proficiency in applying Machine Learning techniques in real-world scenarios. Utilizing Python and its extensive ecosystem of libraries, along with numerous practical examples, the curriculum covers the essential components of Machine Learning. Participants will learn how to make informed decisions regarding data modeling, interpret algorithmic outputs, and validate results effectively.

The objective is to equip you with the confidence to leverage core Machine Learning tools while helping you avoid common pitfalls associated with Data Science applications.

This course aims to equip participants with general proficiency in applying Machine Learning methods in real-world scenarios. By leveraging the Python programming language and its extensive ecosystem of libraries, and supported by a wide range of practical examples, the course demonstrates how to utilize the essential components of Machine Learning. Participants will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to empower you with the confidence to understand and apply the core tools of the Machine Learning toolkit, while helping you steer clear of common pitfalls associated with Data Science applications.

This instructor-led live training in Lyon (online or onsite) is intended for data scientists and software engineers who wish to apply AdaBoost for developing boosting algorithms in machine learning using Python.

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

• Set up the required development environment to commence building machine learning models with AdaBoost.
• Comprehend the ensemble learning approach and learn how to implement adaptive boosting.
• Discover how to construct AdaBoost models to enhance machine learning algorithms in Python.
• Utilize hyperparameter tuning to boost the accuracy and performance of AdaBoost models.

Spanning eight days, this programme offers a comprehensive pathway from solid Python engineering principles to sophisticated AI system architecture. Participants will cultivate disciplined coding habits, gain expertise in statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production. The curriculum emphasizes reliability, evaluation, safety, and practical deployment over mere experimentation.

This instructor-led live training in Lyon (online or onsite) is designed for data scientists and non-technical professionals who wish to utilize Auto-Keras to automate the selection and optimization of machine learning models.

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

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This course integrates theoretical foundations with practical application, utilizing specific examples throughout the program.

This instructor-led, live training in Lyon (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 Lyon (online or onsite) is designed for data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Import datasets into DataRobot to analyze, assess, and perform quality checks on data.
• Construct and train models to identify key variables and achieve prediction goals.
• Interpret models to derive actionable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in Lyon (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve superior machine learning models.

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

• Establish an optimal development environment, including all necessary Python packages.
• Gain significant insights by analyzing dataset features.
• Optimize machine learning models by adapting the raw data itself.
• Clean and transform datasets in preparation for machine learning tasks.

Machine learning represents a subset of Artificial Intelligence that enables computers to learn from data without being explicitly programmed for every specific task.

Deep learning is a specialized branch of machine learning that employs methods based on learning data representations and structures, such as neural networks.

Python is a high-level programming language renowned for its clear syntax and high code readability.

In this instructor-led live training, participants will discover how to implement deep learning models for the telecommunications sector using Python by guiding them through the development of a deep learning credit risk model.

Upon completing this training, participants will be able to:

• Comprehend the fundamental concepts of deep learning.
• Identify the applications and uses of deep learning within the telecom industry.
• Utilize Python, Keras, and TensorFlow to construct deep learning models for telecom.
• Develop their own deep learning customer churn prediction model using Python.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Lyon (online or onsite) is designed for data scientists, data analysts, and developers interested in exploring AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement various services for different data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

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 course targets individuals with a prior background in data science and statistics. The explanations provided are designed to either refresh the knowledge of those already familiar with the concepts or inform those with a suitable background.

This instructor-led, live training in Lyon (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Lyon (online or onsite) is designed for engineers who want to deploy Machine Learning workloads to an AWS EC2 server.

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

• Installing and configuring Kubernetes, Kubeflow, and other necessary software on AWS.
• Utilizing EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS.
• Creating and deploying a Kubernetes pipeline to automate and manage ML models in production.
• Training and deploying TensorFlow ML models across multiple GPUs and machines operating in parallel.
• Using other AWS managed services to expand an ML application.

This instructor-led live training in Lyon (online or onsite) is targeted at engineers who wish to deploy Machine Learning workloads to the Azure cloud.

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

• Install and configure Kubernetes, Kubeflow, and other required software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the initialization of a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led live training in Lyon (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.

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Lyon (online or onsite) is designed for technical professionals seeking to implement a machine learning strategy while optimizing the utilization of big data.

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

• Comprehend the evolution and current trends in machine learning.
• Gain insight into how machine learning is applied across various industries.
• Familiarize themselves with the tools, skills, and services required to implement machine learning within an organization.
• Understand how machine learning can enhance data mining and analysis processes.
• Learn about data middle backends and their business applications.
• Grasp the role that big data and intelligent applications play across different sectors.

This training program is designed for individuals who wish to apply Machine Learning in practical scenarios for their teams. The course focuses on fundamental concepts and their business and operational applications, rather than delving into technical intricacies.

Target Audience

1. Investors and AI entrepreneurs
2. Managers and Engineers whose companies are entering the AI sector
3. Business Analysts & Investors

Machine learning constitutes a subset of Artificial Intelligence where systems possess the capability to learn without explicit programming instructions. Python is a programming language renowned for its clean syntax and high readability. It provides an extensive array of rigorously tested libraries and methodologies essential for building machine learning applications.

Through this instructor-led live training, participants will acquire the skills necessary to apply machine learning techniques and tools to address real-world challenges within the finance sector.

Participants will first grasp the core principles before applying their knowledge by constructing their own machine learning models and utilizing them to complete various team-based projects.

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

• Comprehend the fundamental concepts of machine learning
• Explore the applications and utility of machine learning in finance
• Develop an algorithmic trading strategy using machine learning with Python

Audience

• Developers
• Data scientists

Format of the course

• A blend of lectures, discussions, exercises, and intensive hands-on practice

This instructor-led live training, available online or onsite, is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This training course is designed for individuals who wish to apply fundamental machine learning techniques in practical scenarios.

Audience

Data scientists and statisticians who possess some familiarity with machine learning and are proficient in programming with R. The emphasis of this course is on the practical aspects of data and model preparation, execution, post-hoc analysis, and visualization. The purpose is to provide a practical introduction to machine learning for participants interested in applying these methods at work.

Sector-specific examples are used to make the training relevant to the audience.

In this instructor-led, live training, participants will learn how to leverage the iOS Machine Learning (ML) technology stack by stepping through the creation and deployment of a functional iOS mobile app.

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

• Create a mobile app capable of image processing, text analysis, and speech recognition
• Access pre-trained ML models for integration into iOS apps
• Create a custom ML model
• Add Siri Voice support to iOS apps
• Understand and utilize frameworks such as CoreML, Vision, CoreGraphics, and GameplayKit
• Use languages and tools such as Python, Keras, Caffe, TensorFlow, scikit-learn, libsvm, Anaconda, and Spyder

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises, and heavy hands-on practice

This instructor-led live training, offered online or onsite, is intended for developers who aim to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in Lyon (online or onsite) targets intermediate-level business and technical professionals who wish to apply machine learning techniques to solve real-world business challenges using practical case studies and hands-on tools.

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

• Understand how machine learning fits into modern AI systems and business strategies.
• Identify appropriate machine learning methods for different business problems.
• Preprocess and transform business data for machine learning tasks.
• Apply core machine learning techniques such as classification, regression, clustering, and time series forecasting.
• Interpret and evaluate machine learning models in the context of business decision-making.
• Gain hands-on experience through case studies and apply learned techniques to practical scenarios.

This course presents machine learning techniques applied to robotics.

It offers a comprehensive overview of current methods, their underlying motivations, and core concepts within the field of pattern recognition.

Following a brief theoretical foundation, participants will engage in practical exercises using open-source tools (typically R) or other widely used software.

This instructor-led, live training in Lyon (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.

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course also allows for the evaluation of participants.

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

This instructor-led, live training in Lyon (online or onsite) is designed for data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random Forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training, participants will learn how to leverage RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course also allows for the evaluation of participants.

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.