Introduction machine learning python par guido sarah (37 résultats)

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paperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Paperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Paperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

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Paperback. Etat : Good. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas Müller; Sarah Guido. O'Reilly Media, 2016. 398pp. Language: English. Condition Notes.

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Softcover. Etat : VERY GOOD. Very Good. Unmarked. Clean, unmarked interior. Softcover, clean & bright, some edge corner and shelf wear. No rips, chips, stains or tears. Binding solid. 2017 Edition (4th release 2018). Ships from USA, quickly and with care. _____________________________________________________________________________________________________________________________ SYNOPSIS & HISTORY: Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido is a practical and accessible guide to modern machine learning techniques using Python and the scikit-learn library. Designed for students, programmers, analysts, and aspiring data scientists, the book emphasizes hands-on applications and real-world workflows rather than purely theoretical concepts. Covering classification, regression, clustering, model evaluation, parameter tuning, text processing, and data representation, the book demonstrates how machine learning can be applied to solve practical data problems efficiently. Featuring code examples, visual explanations, and project-based learning, this widely respected resource serves as an excellent introduction to applied machine learning and data science with Python. …

Paperback. Etat : Very Good+. First Edition; Third Printing. 378 pages; minor creasing to back cover's bottom corner. Very Good condition otherwise. No other noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence.…

Paperback. Etat : Very Good+. First Edition; First Printing. 376 pages; Very Good condition. No noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence.…

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Etat : New. 400.

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Etat : New. 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. Num Pages: 400 pages. BIC Classification: UMW. Category: (XV) Technical / Manuals. Dimension: 233 x 178. . . 2016. 1st Edition. Paperback. . . . .…

Etat : New. Idioma/Language: Inglés. Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.…