Part 2: Machine Learning with Polyglot Notebooks and ML.NET

With the basics of Polyglot Notebook development and data analysis behind us, we now move on to explore the field of machine learning.

In this part, we’ll discuss the overall flow of machine learning and the machine learning tasks of regression, binary classification, and multi-class classification using ML.NET, Microsoft’s open-source machine learning framework.

We’ll start with the basics and let ML.NET AutoML automatically select the optimal model trainer and hyperparameters for our machine learning tasks.

Once we get the basics down, we’ll explore what AutoML is doing under the hood and study building our own machine learning pipelines and gaining additional control over hyperparameter tuning.

We’ll end this part with an exploration of deploying ML.NET models to production and how to monitor and maintain them after they’re out in the wild.

This part has the following...