Beyond AutoML - Pipelines, Trainers, and Transforms
Resources for Chapter 9 of Data Science in .NET with Polyglot Notebooks
Chapter 9 of Data Science in .NET with Polyglot Notebooks takes the training wheels off of ML.NET by showing how ML.NET works with pipelines, how AutoML selects which model trainer to use, how to work without AutoML at all, and how to perform hyperparameter tuning. This chapter also discusses the available classification and regression model trainers as well as the different hyperparameter tuners.
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Data Science in .NET with Polyglot Notebooks teaches experienced .NET devs the fundamentals of data science, machine learning, and AI orchestration. It covers topics like ML.NET, OpenAI, Semantic Kernel, career development, and more.
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Additional Resources
These resources and notes are likely to be helpful while reading Chapter 9 of Data Science in .NET with Polyglot Notebooks.
- How to Choose an ML.NET Algorithm
- How to use the ML.NET Automated Machine Learning (AutoML) API
- Decision Trees vs Random Forests
- Classification algorithms in ML .NET
- Online Gradient Descent
- LightGBM
- Tweedie Distributions
- Ordinary Least Squares Regression
- Poisson Regression
- Field Factorization Machines
- SMAC: Sequential Model-based Algorithm Configuration
