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Añadir al carritopaperback. Condición: Very Good. Nice copy of this softcover. Binding is tight. Covers are clean and crisp. Pages appear bright and unmarked. Ships FAST! Bringing good books to happy readers since 2002.
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Librería: Zoom Books Company, Lynden, WA, Estados Unidos de America
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Librería: Coffee Cat Books, Chapel Hill, NC, Estados Unidos de America
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Añadir al carritopaperback. Condición: 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). hips from USA, quickly and with care. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido provides practical guidance on implementing machine learning solutions using Python and the scikit-learn library, focusing on real-world applications over theoretical concepts.
Librería: Meadowland Media, Fayetteville, AR, Estados Unidos de America
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Añadir al carritopaperback. it'S NEW Ships same or next bu.
Idioma: Inglés
Publicado por O'Reilly Media, O'Reilly Media, 2016
ISBN 10: 1449369413 ISBN 13: 9781449369415
Librería: Cloud Runner Books, Minneapolis, MN, Estados Unidos de America
EUR 26,65
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Añadir al carritoPaperback. Condición: Good. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas Müller; Sarah Guido. O'Reilly Media, 2016. 398pp. Language: English. Note:
Idioma: Inglés
Publicado por O'Reilly Media, Sebastopol, CA, 2016
ISBN 10: 1449369413 ISBN 13: 9781449369415
Librería: True Oak Books, Highland, NY, Estados Unidos de America
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por O'Reilly Media, Sebastopol, CA, 2016
ISBN 10: 1449369413 ISBN 13: 9781449369415
Librería: True Oak Books, Highland, NY, Estados Unidos de America
Miembro de asociación: IOBA
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por Shroff Publishers & Distributors Pvt Ltd, 2016
ISBN 10: 9352134575 ISBN 13: 9789352134571
Librería: medimops, Berlin, Alemania
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Idioma: Inglés
Publicado por Shroff Publishers & Distributors Pvt Ltd, 2016
ISBN 10: 9352134575 ISBN 13: 9789352134571
Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de America
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Añadir al carritoPaperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 43,29
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Añadir al carritoCondición: new.
Idioma: Inglés
Publicado por O'Reilly Media, Sebastopol, 2016
ISBN 10: 1449369413 ISBN 13: 9781449369415
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 51,80
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Añadir al carritoPaperback. Condición: new. Paperback. 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.Youll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Mller 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, youll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skillsAbout the AuthorsSarah is a data scientist at Reonomy, where she's helping to build disruptive tech in the commercial real estate industry in New York City. Three of her favorite things are Python, data, and machine learning. Andreas Mueller 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 joined the Center for Data Science at the New York University, and later the Columbia University Data Science Institute. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 53,76
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Añadir al carritoPaperback. Condición: New. 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 Muller 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.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 38,87
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Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 56,33
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Añadir al carritoPaperback. Condición: New. 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 Muller 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.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: moluna, Greven, Alemania
EUR 47,60
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Añadir al carritoCondición: New. 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 w.
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 57,92
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Añadir al carritoPaperback. Condición: New. 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 Muller 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.
Librería: Mooney's bookstore, Den Helder, Holanda
EUR 93,81
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Añadir al carritoCondición: Very good.