Data Preparation, Feature Engineering, AWS Glue, and Amazon EMR

In this presentation, I explain the importance of data preparation and feature engineering in machine learning workflows. The video covers why clean, high-quality data is essential for building accurate ML models, common data quality problems, data cleaning techniques, data transformation, feature engineering, encoding methods, bias in data, and protecting sensitive information like PII. I also discuss AWS services used for data preparation and large-scale processing, including AWS Glue, AWS Glue DataBrew, AWS Glue Data Quality, Amazon EMR, and Apache Spark. These services help clean, transform, validate, catalog, and process data for machine learning and analytics workflows on AWS. #AWSMachineLearning #AWSMLAssociate #AWSCertification #DataPreparation #FeatureEngineering #AWSGlue #AWSDataBrew #AmazonEMR #ApacheSpark #MachineLearning #MLOps #DataQuality #CloudComputing #DataScience #MLWorkflow