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. Cambridge onsite live ML.NET trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Cambridge
Compass House, Cambridge, united kingdom, CB24 9AD
Vision Park is within easy reach of
Cambridge city centre and has the
advantage of excellent links with the
M11 and A14.
...
Vision Park is within easy reach of
Cambridge city centre and has the
advantage of excellent links with the
M11 and A14.
Trains from London Kings Cross arrive every 30 minutes, with a 15 minute journey by taxi to the Park. Histon Railway Station is on the boundary of Vision Park, which provides high quality, frequent local transport from St Ives to Cambridge.
Guided Bus
Vision Park lies alongside the Cambridgeshire Guided Bus which offers fast and frequent buses from Histon to Huntingdon (50 minutes), St Ives (15) and Cambridge city centre (15). Onboard, there is air conditioning, free WiFi and power for your laptop and mobile devices. Please see www.thebusway.info for further information.
This instructor-led, live training in Cambridge (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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