AWS Feature Engineering and Data Transformation

AWS: Feature Engineering, Data Transformation & Integrity is the second course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course enables learners to build essential skills in preparing and transforming data for machine learning workloads using AWS services. It provides a structured, hands-on understanding of data cleaning, feature engineering, encoding techniques, and scalable ETL workflows on AWS. Learners will start by mastering data preparation techniques, including cleaning, transformation, and feature extraction. The course explores methods to improve model accuracy by engineering meaningful features and applying categorical encoding strategies such as One-Hot Encoding, Label Encoding, and Tokenization. Learners will also understand the importance of maintaining data integrity and fairness, addressing bias, and securely handling sensitive information (PII) using tools like AWS Glue DataBrew. In the second module, learners will gain practical experience with AWS-native tools for scalable data engineering. This includes working with AWS Glue for ETL job orchestration, Glue Data Quality for dataset validation, and AWS Glue DataBrew for code-free data profiling and transformation. Learners will also dive into Amazon EMR, processing large-scale datasets using Apache Spark to build powerful, distributed data pipelines tailored for ML workflows. The course is divided into two modules, each broken down into lessons and practical video walkthroughs. Learners can expect approximately 2.5 to 3 hours of video lectures, combining theoretical knowledge with hands-on guidance using AWS ML services. Each module also includes Graded and Ungraded Quizzes to reinforce understanding and assess readiness. Module 1: Data Preparation & Transformation Techniques Module 2: ETL & Data Engineering with AWS Glue and EMR By the end of this course, learners will be able to: - Clean, transform, and engineer data effectively for ML use cases - Apply categorical encoding techniques for machine learning models - Ensure fairness, integrity, and compliance in dataset preparation - Use AWS Glue, Glue DataBrew, and EMR for scalable, production-ready data pipelines This course is ideal for machine learning practitioners, data engineers, and developers with 6 months to 1 year of AWS experience. It is also valuable for learners preparing for the MLA-C01 exam who want to deepen their hands-on skills in data transformation, feature engineering, and large-scale ETL on AWS

• Apply data cleaning, transformation, and feature engineering techniques
• Identify bias reduction and secure PII management methods using AWS tools
• Implement ETL workflows with AWS Glue, Glue Crawlers, and DataBrew
• Process large datasets using Apache Spark on Amazon EMR

• Basic familiarity with machine learning concepts and terminology
• Readiness for applied exercises and case studies