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 Csongrád or in NobleProg corporate training centers in Csongrád.
NobleProg -- Your Local Training Provider
Szeged
NobleProg Szeged, Maros utca 1., Szeged, hungary, 6721
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and...
The NobleProg conference rooms are located in the city center, right next to the garden of the beautiful castle of Szeged and close to the Tisza river. These conference rooms can be hired as lecture, seminar, teaching or meeting rooms. With natural lighting from two large windows, a variety of artificial lighting options and great technical equipments, such as projector, computers, full sound system with speakers, and internet, this meeting room has great possibilities for successful presentations or trainings. Our professional staff is taking care of the preparation and implementation of the high quality conferences.
This instructor-led, live training in Csongrád (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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