Da: Better World Books, Mishawaka, IN, U.S.A.
EUR 18,20
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Aggiungi al carrelloCondizione: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
Da: Best Price, Torrance, CA, U.S.A.
EUR 36,02
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 39,96
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Aggiungi al carrelloCondizione: New.
Editore: Packt Publishing 7/24/2020, 2020
ISBN 10: 1838826041 ISBN 13: 9781838826048
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 44,16
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine lear 1.45. Book.
Da: California Books, Miami, FL, U.S.A.
EUR 44,95
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Da: Toscana Books, AUSTIN, TX, U.S.A.
EUR 47,96
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Aggiungi al carrelloPaperback. Condizione: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Da: Russell Books, Victoria, BC, Canada
EUR 48,42
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Aggiungi al carrelloPaperback. Condizione: New. Special order direct from the distributor.
Da: Chiron Media, Wallingford, Regno Unito
EUR 39,04
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 44,20
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Aggiungi al carrelloCondizione: New. In.
Da: moluna, Greven, Germania
EUR 48,76
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Aggiungi al carrelloCondizione: New. This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics.
Da: Majestic Books, Hounslow, Regno Unito
EUR 52,61
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 384.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 48,76
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 62,74
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm's hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is forThis book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.