Machine Learning Training in Montpellier

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

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

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

This instructor-led, live training in Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 Montpellier (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 course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions entirely from scratch using Python. It explores the core principles of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical ML models, assessing algorithmic limitations, and completing applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning models, empowering agents to make decisions by interacting with their surroundings. This technology drives many modern AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, refine policies, and make autonomous choices through trial and error, guided by reward-based feedback.

This instructor-led training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to build intelligent agents capable of making autonomous decisions in complex environments.

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

• Grasp the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

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

• Interactive lectures accompanied by guided discussions.
• Hands-on exercises and practical implementation tasks.
• Live coding demonstrations and project-based applications.

Course Customization Options

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

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automating processes, and enhancing decision-making across enterprise operations. This course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to accelerate business agility.

This instructor-led live training in Montpellier (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.

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

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.

An Artificial Neural Network is a computational data model utilized in creating Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which constitute one implementation of AI. Deep Learning represents a specialized subset of ML.

Enhance your data science skills with this in-depth Machine Learning training program. The course covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Participants will acquire practical experience grounded in theoretical principles, utilizing real-world case studies. This course is particularly beneficial for data analysts, software engineers, AI aficionados, and business professionals aiming to implement machine learning solutions. You will master key concepts including classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning foundations to construct reliable predictive models.

This instructor-led live training in Montpellier (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Montpellier (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 Montpellier (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.

Audience

This course is ideal for deep learning researchers and engineers who wish to leverage accessible tools, primarily open-source solutions, to analyze computer images.

The course includes practical, hands-on examples.

This instructor-led live training in Montpellier (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led live training in Montpellier (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.

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 Montpellier (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 instructor-led, live training in Montpellier (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

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 Montpellier (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 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.

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 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 Montpellier (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 Montpellier (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.

This course provides foundational knowledge on neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40%) of the training emphasizes fundamentals, enabling you to select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part 2 (20%) introduces Theano, a Python library designed to simplify the development of deep learning models.

Part 3 (40%) focuses extensively on TensorFlow, Google's open-source software library API for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilize TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of managing installation, production environments, and architectural configurations
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in implementing advanced production tasks such as training models, constructing graphs, and logging