EUR 23,37
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EUR 26,22
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: BooksRun, Philadelphia, PA, U.S.A.
Prima edizione
EUR 28,54
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Aggiungi al carrelloPaperback. Condizione: Good. 1st ed. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
Da: California Books, Miami, FL, U.S.A.
EUR 28,83
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Da: Best Price, Torrance, CA, U.S.A.
EUR 22,15
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Aggiungi al carrelloCondizione: New. SUPER FAST SHIPPING.
EUR 32,67
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 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 forecasting Understand 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.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
EUR 23,39
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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.
EUR 29,58
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ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: Basi6 International, Irving, TX, U.S.A.
EUR 24,01
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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.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 34,94
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Aggiungi al carrelloCondizione: New. In.
EUR 31,99
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Books Puddle, New York, NY, U.S.A.
EUR 55,65
Convertire valutaQuantità: 4 disponibili
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Da: Revaluation Books, Exeter, Regno Unito
EUR 36,62
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Aggiungi al carrelloPaperback. Condizione: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
EUR 32,39
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
EUR 29,61
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 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 forecasting Understand 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 55,53
Convertire valutaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 58,42
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.