EUR 20,97
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EUR 24,31
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 26,73
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python 0.61. Book.
Da: Lakeside Books, Benton Harbor, MI, U.S.A.
EUR 23,30
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Aggiungi al carrelloCondizione: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
Da: California Books, Miami, FL, U.S.A.
EUR 30,26
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 33,23
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 2 working days. 184.
Da: Russell Books, Victoria, BC, Canada
Prima edizione
EUR 34,84
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Aggiungi al carrellopaperback. Condizione: New. 1st ed. Special order direct from the distributor.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 36,14
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Da: Revaluation Books, Exeter, Regno Unito
EUR 38,69
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Aggiungi al carrelloPaperback. Condizione: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
EUR 33,22
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ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
EUR 26,25
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Aggiungi al carrelloCondizione: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: Basi6 International, Irving, TX, U.S.A.
EUR 26,25
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Aggiungi al carrelloCondizione: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
EUR 35,04
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Da: Chiron Media, Wallingford, Regno Unito
EUR 42,89
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 49,51
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Da: Books Puddle, New York, NY, U.S.A.
EUR 61,49
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EUR 32,39
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 34,86
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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.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 52,95
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 50,24
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 37,44
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing.It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations.After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python.What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecastingUnderstand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis. 192 pp. Englisch.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 38,35
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing.It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations.After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python.What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecastingUnderstand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Da: Majestic Books, Hounslow, Regno Unito
EUR 61,94
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 63,54
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.