Feature Engineering with TensorFlow: Processing Numeric Inputs for ML

Raw numeric data is rarely ready for machine learning models right out of the box. To build accurate predictive systems, you must first learn how to properly structure, normalize, and pipeline your numerical inputs. This text-only course guides you through the foundational concepts of feature engineering using TensorFlow. You will learn how to transition from raw tabular datasets to highly optimized model inputs, ensuring your algorithms train faster and perform with greater accuracy. What you'll learn: - Understand the fundamentals of numeric feature columns and data representation - Clean and preprocess raw tabular data using modern dataframe libraries - Apply scaling, normalization, and bucketization techniques to numerical features - Configure TensorFlow input pipelines to handle structured datasets efficiently - Implement feature columns to map raw data to model-ready tensors - Practice building robust input layers using realistic retail sales scenarios Starting with core definitions and basic data concepts, this written program takes you step-by-step through the process of preparing numeric features for model consumption. This course is designed for beginners and aspiring data professionals who want to master the critical first steps of the machine learning pipeline. Start reading today to build cleaner, more efficient input layers for your machine learning models.