This chapter will be a deep dive into the development of classic machine learning algorithms to train and deploy models based on tabular data, exploring libraries and algorithms as well. The examples will be focused on the particularities and advantages of using Azure Databricks Runtime for Machine Learning (Databricks Runtime ML).
In this chapter we will explore the following concepts, which are focused on how we can extract and improve the features available in our data to train our machine learning and deep learning models. The topics that we will cover are listed here:
- Loading data
- Feature engineering
- Time-series data sources
- Handling missing values
- Extracting features from text
- Training machine learning models on tabular data
In the following sections, we will discuss the necessary libraries needed to perform the operations introduced, as well as providing some context on how best practices...