Automated ML.NET Training, Metrics, and Accuracy

Course details

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.

Concepts

0. Introduction

01 - Leveraging automation, metrics, and accuracy in ML.NET
02 - What you should know
03 - Using the exercise files

1. Using AutoML

04 - What is AutoML
05 - How to use AutoML API
06 - Demo - AutoML

2. Automate Model Training with the ML.NET CLI

07 - How to install the ML.NET CLI tool
08 - Demo - Analyze sentiment using the ML.NET CLI
09 - Telemetry in ML.NET

3. Evaluate the ML.NET Model with Metrics

10 - Model metrics
11 - Demo - Metrics
12 - Permutation feature importance (PFI)
13 - Demo - Permutation feature importance (PFI)

4. Improve Model Accuracy

14 - Reframe the problem
15 - Provide better data samples
16 - Cross-validation
17 - Demo - Cross-validation
18 - Hyperparameter tuning
19 - Inspect pipeline values
20 - Demo - Inspect pipeline values
21 - Choose a different algorithm

Conclusion

22 - CLI pitfalls
23 - Next steps

Automated ML.NET Training, Metrics, and Accuracy

Authors

Sam Nasr