EUR 23,44
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Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
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EUR 25,79
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In den WarenkorbPaperback. Zustand: 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.
EUR 26,63
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: California Books, Miami, FL, USA
EUR 29,28
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In den WarenkorbZustand: New.
Zustand: New.
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 30,59
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In den WarenkorbPaperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
Anbieter: California Books, Miami, FL, USA
EUR 33,72
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In den WarenkorbZustand: New.
Zustand: As New. Unread book in perfect condition.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 33,75
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In den WarenkorbZustand: New. In.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 37,05
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 32,34
Anzahl: 1 verfügbar
In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 33,34
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In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 36,20
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In den WarenkorbZustand: New.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: 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.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 38,34
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: Books Puddle, New York, NY, USA
Zustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 43,72
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In den WarenkorbZustand: New. In.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 45,43
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
Zustand: New.
EUR 32,41
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In den WarenkorbZustand: New.
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
Erstausgabe
EUR 22,89
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: 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.
EUR 25,26
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 55,69
Anzahl: 4 verfügbar
In den WarenkorbZustand: New. Print on Demand.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 69,65
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In den WarenkorbZustand: New. Print on Demand.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Anbieter: moluna, Greven, Deutschland
EUR 40,39
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In den WarenkorbKartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You ll start by learning basic concepts of recommende.