Supervised Learning Training in Nantes

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, 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 (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 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 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) 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 Nantes (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, 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.