Overview

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.

Syllabus

Data Preparation & Transformation Techniques

Welcome to Week 1 of the AWS: Feature Engineering, Data Transformation & Integrity course. This week, you’ll dive into the foundational steps of preparing high-quality data for machine learning workflows. We’ll begin with data cleaning and transformation techniques to ensure consistency and accuracy in your datasets. You’ll then explore feature engineering methods that help extract meaningful insights, followed by encoding techniques such as One-Hot Encoding, Label Encoding, and Tokenization to prepare categorical and textual data for modeling. Finally, we’ll focus on ensuring data integrity and fairness by learning how to address bias in data preparation and securely handle sensitive information (PII) using tools like AWS Glue DataBrew.

ETL & Data Engineering with AWS Glue and EMR

Welcome to Week 2 of the AWS: Feature Engineering, Data Transformation & Integrity course. This week, you'll dive into AWS-native tools for large-scale data processing and transformation. We’ll begin with AWS Glue, where you'll learn how to create Glue Crawlers, configure ETL jobs, and validate outputs for structured and semi-structured data. You'll explore AWS Glue DataBrew, a no-code tool that simplifies data profiling, cleaning, and transformation. We’ll also cover AWS Glue Data Quality to help ensure your datasets meet required standards for ML workflows. In the second half of the week, you’ll work with Amazon EMR to process massive datasets using Apache Spark. You'll launch EMR clusters, submit jobs, and transform data at scale — gaining hands-on experience with distributed data pipelines tailored for machine learning tasks.