Feature Engineering for Machine Learning and Cloud Analytics

Raw data is rarely ready for machine learning models, and the quality of your features often determines the success of your AI projects. This course provides a comprehensive guide to identifying, creating, and managing the data attributes that drive impactful predictions. You will learn how to select, transform, and manage data attributes to build more accurate and scalable models. By the end of the course, you will understand how to bridge the gap between raw data storage and model-ready input. What you'll learn: - Understand foundational concepts of feature selection and data transformation - Practice engineering attributes using Keras and TensorFlow frameworks - Apply feature management techniques with Vertex AI Feature Store - Build machine learning models directly within data warehouses using BigQuery ML - Implement automated feature selection to identify the most impactful data columns - Explore modern data validation techniques to ensure feature consistency over time The course begins with essential terminology and data preparation basics before moving into practical cloud-based feature management and model integration. You will read through detailed explanations and study code examples that demonstrate how to handle real-world data challenges. This course is designed for beginners in data science and machine learning; no prior experience with specialized cloud platforms or advanced engineering is required. Start building better machine learning models today by mastering the art of feature engineering.