pTake a data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI. This hands-on guide presents three problem-focused ways to learn ML no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems. p Business and data analysts get a project-based introduction to MLAI using a detailed, data-driven approach loading and analyzing data, feeding data into an ML model building, training, and testing and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. p You'll learn how to ul liDistinguish structured and unstructured data and understand the different challenges they present liVisualize and analyze data liPreprocess data for input into a machine learning model liDiffe