Data Processing with TensorFlow: Handling and Scaling Float Features

Raw numerical data is rarely ready for machine learning models without proper preprocessing. Learning how to ingest, scale, and transform continuous float features is a foundational step in building efficient machine learning pipelines. This written course teaches you how to handle continuous variables—such as temperature, prices, and physical measurements—and convert them into optimized TensorFlow objects ready for model training. What you will learn: Understand the fundamentals of float features and how TensorFlow represents continuous numerical data; Convert raw numerical datasets into high-performance TensorFlow Dataset objects; Apply modern preprocessing layers to normalize and scale float features; Handle missing numerical values and NaN data points safely within your pipelines; Configure input pipelines using the tf.data API for optimal training efficiency. You will start with basic data types and core definitions before moving on to practical step-by-step written guides that demonstrate how to clean, scale, and feed float data directly into machine learning models. This course is designed for beginner data analysts and aspiring machine learning engineers, requiring no prior TensorFlow experience. Start mastering numerical data processing for machine learning today.