Low-Code AI by Gwendolyne Stripling
Take 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. Business and data analysts get a project-based introduction to ML/AI 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. You'll learn how to: Distinguish structured and unstructured data and understand the different 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 machine learning 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| SKU | Nicht verfügbar |
| ISBN 13 | 9781098146825 |
| ISBN 10 | 1098146824 |
| Titel | Low-Code AI |
| Autor | Gwendolyne Stripling |
| Buchzustand | Nicht verfügbar |
| Bindungsart | Paperback |
| Verlag | O'Reilly Media |
| Erscheinungsjahr | 2023-09-29 |
| Seitenanzahl | 325 |
| Hinweis auf dem Einband | Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden. |
| Hinweis | Nicht verfügbar |