Low-Code AI

A Practical Project-Driven Introduction to Machine Learning

von Gwendolyn Stripling, Michael Abel

325 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. > You'll learn how to:

- Distinguish between structured and unstructured data and the challenges they present - Visualize and analyze data - Preprocess data for input into a machine learning model - Differentiate between the regression and classification supervised learning models - Compare different ML model types and architectures, from no code to low code to custom training - Design, implement, and tune ML models - Export data to a GitHub repository for data management and governance