LinkedIn Learning – Automated ML.NET Training, Metrics, and Accuracy

LinkedIn Learning – Automated ML.NET Training, Metrics, and Accuracy
English | Tutorial | Size: 406.81 MB

Knowing how to use Microsoft ML.NET is one of the most in-demand skills in today’s job market. If you’re looking to boost your know-how with machine learning, ML.NET is a must-have in your toolbox. In this course, senior software engineer and Microsoft MVP Sam Nasr provides a comprehensive overview of some of the advanced features of ML.NET, including how to use the command line interface, gather metrics, and evaluate and improve model accuracy and performance. Get started with an introduction to automated machine learning (AutoML) before learning how to automate model training with the ML.NET command-line interface. Along the way, Sam shows you how to evaluate an ML.NET model with metrics and improve model accuracy with cross-validation, hyperparameter tuning, pipeline value inspection, and more.

Learning Objectives:
• Build a model using automated machine learning (AutoML).
• Evaluate the model.
• Collect metrics on model performance.
• Improve model accuracy.

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