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Time Series Algorithms Recipes by Kulkarni Akshay (24 results)
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Time Series Algorithms Recipes 1st ed.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Paperback / softback. Condition: New. New copy - Usually dispatched within 2 working days. 184.
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Time Series Algorithms Recipes 1st ed.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
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£ 22.41
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Add to basketPaperback or Softback. Condition: New. Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python 0.61. Book.
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Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python
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Paperback. Condition: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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International EditionTime Series Algorithms Recipes
ISBN 10: 1484294130 ISBN 13: 9781484294130
£ 21.75
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Add to basketCondition: 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.
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International Edition
Time Series Algorithms Recipes
ISBN 10: 1484294130 ISBN 13: 9781484294130
£ 21.75
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Add to basketCondition: 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.
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Time Series Algorithms Recipes
Published by Springer, Berlin|Apress, 2023
ISBN 10: 1484289773 ISBN 13: 9781484289778
Language: English
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
First Edition
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Add to basketCondition: 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!
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Time Series Algorithms Recipes: Implementation Machine Learning and Deep Learning Techniques with Python
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Time Series Algorithms Recipes
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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£ 32.49
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Add to basketTaschenbuch. Condition: 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.
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Time Series Algorithms Recipes
Published by Springer Nature B.V., 2022
ISBN 10: 148428979X ISBN 13: 9781484289792
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
£ 41.01
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Time Series Algorithms Recipes
Published by Springer Nature B.V., 2022
ISBN 10: 148428979X ISBN 13: 9781484289792
Language: English
£ 44.40
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Add to basketPAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python
£ 33.28
Convert currency£ 11.79 shipping from Germany to United KingdomQuantity: 1 available
Add to basketTaschenbuch. Condition: 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.
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£ 55.15
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