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Ajouter au panierPaperback. Etat : New. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
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Ajouter au panierPaperback. Etat : New. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
Vendeur : California Books, Miami, FL, Etats-Unis
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Langue: anglais
Edité par O'Reilly Media, Sebastopol, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
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Ajouter au panierPaperback. Etat : new. Paperback. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Langue: anglais
Edité par O'Reilly Media, Inc, USA, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
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Ajouter au panierPaperback. Etat : New. New copy - Usually dispatched within 4 working days.
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Langue: anglais
Edité par O'Reilly Media 1/27/2017, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
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Ajouter au panierPaperback or Softback. Etat : New. Thoughtful Machine Learning with Python: A Test-Driven Approach. Book.
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Langue: anglais
Edité par Oreilly & Associates Inc, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
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Ajouter au panierPaperback. Etat : Brand New. 250 pages. 9.00x6.75x0.25 inches. In Stock.
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Ajouter au panierKartoniert / Broschiert. Etat : New. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.Über den Autor.
EUR 45,68
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Ajouter au panierPaperback. Etat : New. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
Langue: anglais
Edité par O'Reilly Media, Sebastopol, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
Vendeur : AussieBookSeller, Truganina, VIC, Australie
EUR 70,91
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Ajouter au panierPaperback. Etat : new. Paperback. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Langue: anglais
Edité par O'reilly Media Feb 2017, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 41,86
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware - Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
EUR 39,94
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Ajouter au panierPaperback. Etat : New. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
Langue: anglais
Edité par Oreilly & Associates Inc, 2017
ISBN 10 : 1491924136 ISBN 13 : 9781491924136
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 46,98
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Ajouter au panierPaperback. Etat : Brand New. 250 pages. 9.00x6.75x0.25 inches. In Stock. This item is printed on demand.