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 Surrey or in NobleProg corporate training centers in Surrey.
NobleProg -- Your Local Training Provider
Surrey - King Cross
7404 King George Blvd, Surrey, Canada, V3W 1N6
In one of Canada's fastest growing major cities, our King's Cross business centre is a practical place to do business in Surr...
In one of Canada's fastest growing major cities, our King's Cross business centre is a practical place to do business in Surrey. As well as on-site businesses from banking, insurance and beyond, regular bus links and the King George Boulevard offer fast, convenient connections to the city centre. Newton Exchange the main bus hub connecting to south Surrey the lower main land is but a block away.
King's Cross is a popular mixed-use community site, home to coffee shops, retailers and fast food - ideal for breaks or informal meetings. And with heavy footfall and visibility from the highway, it combines the exposure you'd expect from a city centre with the convenience of an out of town location.
This instructor-led, live training in Surrey (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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