Machine Learning Basics in PySpark
In this class I cover the basics of pre-processing and running machine learning models in PySpark. First, I review the concept of estimators versus transformers (this is an extremely important topic for the Databricks ML Practitioner Exam). I cover how to perform pre-processing steps including:
One-Hot encoding
Preparing categorical data for processing with StringIndexer
Missing data imputation
Rescaling features
Inputting data into a model with VectorIndexer
Finally, I cover how to run a machine learning model and how to combine all the above steps into a single pipeline optimized for deployment.
To gain access to code, data, and course materials visit https://kelseyemnett.com/2021/07/10/m....
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