Feature Engineering with PySpark

Your job is to find the meaning in chaos. Careful curation is required to create toys datasets like MTCars and Iris. The data must be transformed in order to make them useful for machine-learning algorithms that can predict, extract, classify, cluster, etc. This course will cover the details that data scientists spend between 70 and 80% of their time dealing, such as feature engineering and data wrangling. Let's use PySpark Big Data to reduce these datasets which are becoming increasingly complex.