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. Brighton onsite live ML.NET trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Brighton
3rd Floor, Queensberry House , Brighton, united kingdom, BN1 3XF
This centre occupies a prominent location on Queens Road which links Brighton station to the Churchill Square shopping cen...
This centre occupies a prominent location on Queens Road which links Brighton station to the Churchill Square shopping centre and the seafront. Queensberry House, which offers some sea views, is in the heart of the business district in this city. Brighton is a highly sophisticated and modern resort with a thriving economy and tourist industry. Millions of visitors flock here every year, both for leisure and to attend conferences and exhibitions at the Brighton Centre, one of the largest multi-purpose conference and exhibition centres in the country. Because it is home to so many businesses involved in media, particularly digital or 'new media', Brighton is sometimes referred to as 'Silicon Beach'. The city's priority sectors, which include financial services, creative industries and environmental technologies as well as digital media, are all growing steadily.
This instructor-led, live training in Brighton (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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