Online or onsite, instructor-led live ML.NET training courses demonstrate through interactive hands-on practice how to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications..
ML.NET training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live ML.NET training can be carried out locally on customer premises in Limoges or in NobleProg corporate training centers in Limoges.
NobleProg -- Your Local Training Provider
Limoges, City Center
NobleProg Limoges, 50 Boulevard Gambetta, Limoges, France, 87000
The nearest airport to the training center: Limoges International Airport
Nearest train station: Limoges-Bénédictins train...
The nearest airport to the training center: Limoges International Airport
Nearest train station: Limoges-Bénédictins train station
Nearest parking lot to training center: Parking Place d'Aine
Nearby cafés, restaurants and bars: Restaurant ESCROCS, Restaurant Le Glacier, Bar Le Duc Etienne
Training can also be organized on your premises in Strasbourg or remotely. The 3rd option is to rent a room specifically for the duration of the course.
Training sessions can be given in English or French. For each session, we organize a coordination call with the trainer and participants to adapt the program to your expectations and level. Training includes theory, discussion, questions/answers and practical exercises.
Remote training is delivered via DaDesktop for training, a software package published by NobleProg that enables participants to have a workstation (VM) set up specifically for the training.
This instructor-led, live training in Limoges (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.
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