Whether delivered online or onsite, instructor-led Supervised Learning training courses utilize interactive, hands-on practice to demonstrate how to apply supervised machine learning techniques for effectively training models, generating predictions, and analyzing data patterns.
Supervised Learning training is offered as either "online live training" or "onsite live training." Online live training (also referred to as "remote live training") is conducted via an interactive remote desktop. Onsite live training can take place locally on customer premises in Nantes or at NobleProg corporate training centers in Nantes.
Supervised Learning is also known as Supervised Machine Learning.
NobleProg -- Your Local Training Provider
Nantes, Zenith
NobleProg Nantes, 4 rue Edith Piaf, Saint-Herblain, france, 44821
In the Parc d'Ar Mor zone, near the Zénith.
Car : from the ring road, Porte de Chézine Exit> Boulevard du Zenith > Esplanade Georges Brassens (restaurants) > Rue Edith Piaf on the right. From the N444 road (Nantes > Lorient), Exit #1 > boulevard Marcel Paul > Rue Edith Piaf at the right.
Parking Zénith P1 (free). Once parked, you can recognize the building: it's one of the tree bulding with zinc frontage.
Bicycle: free indoor parking
Public transport :
Tramway R1, Schoelcher station + 10 mn by foot through commercial center Atlantis
Tramway R1, François Mitterrand stop + bus 50, stop at Saulzaie station or bus 71, stop at the Zénith station
Tramway R3, Marcel Paul station + bus 50, Saulzaie station
Chronobus C6, Hermeland station+ bus 71, Zénith station
Bus : lignes 50 (Saulzaie station) or 71 (Zénith station)
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.
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