What you'll learn

You'll build scalable data ingestion pipelines, implement feature engineering techniques, and explore automation strategies, while also addressing ethical considerations that impact model performance and reliability. In this course, Data Engineering for Machine Learning, you’ll gain hands-on expertise in preparing, validating, and transforming raw data into high-quality datasets ready for machine learning models. First, you'll start by understanding core data engineering concepts, exploring methods to gather and ingest data efficiently from diverse sources such as APIs, databases, CSV, and JSON files. Through practical Python demonstrations using VS Code and libraries like Pandas, you'll build scalable data ingestion pipelines capable of managing both batch and real-time data streams. Then, you'll master essential techniques for data cleaning, preprocessing, and validation to ensure accuracy and quality, significantly impacting downstream ML model performance. Finally, you’ll learn best practices for automating pipelines, handling growing data volumes, and integrating feature engineering processes—all while ensuring responsible and compliant data handling through built-in ethical considerations like bias prevention and data privacy. By the course's conclusion, you'll have the hands-on skills and practical knowledge necessary to confidently engineer robust, scalable, and ethically sound data pipelines, effectively preparing data for machine learning projects and setting a foundation for advanced MLOps practices.