Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
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EUR 29,47
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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 32,39
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In den WarenkorbZustand: New.
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
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EUR 31,27
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In den WarenkorbPaperback. Zustand: New. 1st ed. 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: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
Erstausgabe
EUR 32,60
Währung umrechnenAnzahl: 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.
Anbieter: BooksRun, Philadelphia, PA, USA
Erstausgabe
EUR 28,40
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In den WarenkorbPaperback. Zustand: 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.
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
Erstausgabe
EUR 34,50
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In den WarenkorbPaperback. Zustand: New. 1st ed. 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 28,77
Währung umrechnenAnzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
EUR 23,34
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In den WarenkorbZustand: 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: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 34,79
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In den WarenkorbZustand: New. In.
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In den WarenkorbZustand: New.
EUR 40,73
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In den WarenkorbKartoniert / Broschiert. 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 recommende.
Anbieter: California Books, Miami, FL, USA
EUR 33,13
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In den WarenkorbZustand: New.
EUR 26,16
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 27,74
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In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 29,45
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In den WarenkorbZustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 36,51
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In den WarenkorbPaperback. Zustand: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 31,39
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 31,83
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 45,20
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In den WarenkorbZustand: New. In.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 36,41
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In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 37,85
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 44,78
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In den WarenkorbPaperback. Zustand: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
Anbieter: Books Puddle, New York, NY, USA
EUR 55,53
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In den WarenkorbZustand: New.
Anbieter: Books Puddle, New York, NY, USA
EUR 67,35
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Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
EUR 58,39
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In den WarenkorbZustand: New. PRINT ON DEMAND.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 55,29
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In den WarenkorbZustand: New. Print on Demand.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
EUR 71,41
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In den WarenkorbZustand: New. PRINT ON DEMAND.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 67,90
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In den WarenkorbZustand: New. Print on Demand.