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Introduction to Machine Learning with Python: A Guide for Data Scientists de Sarah Guido
Descripción - Reseña del editor Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You’ll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system. BiografÃa del autor Andreas Muller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms. Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
Detalles del Libro
- Name: Introduction to Machine Learning with Python: A Guide for Data Scientists
- Autor: Sarah Guido
- Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informáticas
- Tamaño del archivo: 11 MB
- Tipos de archivo: PDF Document
- Descargada: 714 times
- Idioma: Español
- Archivos de estado: AVAILABLE
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