Practical automated machine learning on Azure using Azure machine learning to quickly build AI solutions

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyon...

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Detalles Bibliográficos
Otros Autores: Mukunthu, Deepak, author (author), Shah, Parashar, author, Tok, Wee-Hyong, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Beijing : O'Reilly [2019]
Edición:First edition
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630771506719
Descripción
Sumario:Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine learning models is an iterative and time-consuming process. Even those who know how to create these models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply Automated Machine Learning to your data right away. Learn how companies in different industries are benefiting from Automated Machine Learning Get started with Automated Machine Learning using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professionals, and developers can use Automated Machine Learning in their familiar tools and experiences Learn how to get started using Automated Machine Learning for use cases including classification and regression.
Notas:Includes index.
Descripción Física:1 online resource (199 pages)
ISBN:9781492055549
9781492055587
9781492055563