Krishnan v adithya (33 Ergebnisse)
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
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EUR 23,96
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Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 25,73
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
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EUR 29,32
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Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 27,13
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Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
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EUR 29,49
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Paperback. 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 recommen…der 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.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
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EUR 31,35
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Paperback. 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.
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 30,88
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Zustand: As New. Unread book in perfect condition.
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
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EUR 33,77
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Zustand: New.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
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EUR 34,06
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Paperback. 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.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 38,17
Versand nach gratisVersand innerhalb von USAAnzahl: 8 verfügbar
Paperback. 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 recommen…der 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.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R, Shivananda, Adarsha, Kulkarni, Anoosh, Krishnan, V Adithya
- Softcover
- Erstausgabe
Anbieter: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandKennys Bookshop and Art Galleries Ltd.
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Zustand: New. 2022. 1st ed. paperback. . . . . .
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R/ Shivananda, Adarsha/ Kulkarni, Anoosh/ Krishnan, V Adithya
- Softcover
Anbieter: Revaluation Books, Exeter, , Vereinigtes KönigreichRevaluation Books
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Paperback. Zustand: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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Zustand: As New. Unread book in perfect condition.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R, Shivananda, Adarsha, Kulkarni, Anoosh, Krishnan, V Adithya
- Softcover
Anbieter: Kennys Bookstore, Olney, MD, USAKennys Bookstore
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EUR 43,01
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Zustand: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 36,82
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Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 37,72
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Zustand: As New. Unread book in perfect condition.
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan,
- Softcover
Anbieter: Chiron Media, Wallingford, , Vereinigtes KönigreichChiron Media
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EUR 39,03
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paperback. Zustand: New.
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay/ Shivananda, Adarsha/ Kulkarni, Anoosh/ Krishnan, V Adithya
- Softcover
Anbieter: Revaluation Books, Exeter, , Vereinigtes KönigreichRevaluation Books
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Paperback. Zustand: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: Books Puddle, New York, NY, USABooks Puddle
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Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: Books Puddle, New York, NY, USABooks Puddle
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Zustand: New. 1st ed. edition NO-PA16APR2015-KAP.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves USA United, OSWEGO, IL, USARarewaves USA United
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 33,11
EUR 43,13 VersandVersand innerhalb von USAAnzahl: 8 verfügbar
Paperback. 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.
Time Series Algorithms Recipes
Kulkarni, Akshay R|Shivananda, Adarsha|Kulkarni, Anoosh|Krishnan, V Adithya
- Softcover
Anbieter: moluna, Greven, , Deutschlandmoluna
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EUR 32,41
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Zustand: New.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves USA United, OSWEGO, IL, USARarewaves USA United
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 40,03
EUR 43,13 VersandVersand innerhalb von USAAnzahl: 8 verfügbar
Paperback. 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 recommen…der 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.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves.com UK, London, Vereinigtes KönigreichRarewaves.com UK
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EUR 26,64
EUR 75,23 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
Paperback. 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 recommen…der 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.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves.com UK, London, Vereinigtes KönigreichRarewaves.com UK
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 33,09
EUR 75,23 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 8 verfügbar
Paperback. 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.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
- Print-on-Demand
Anbieter: Majestic Books, Hounslow, , Vereinigtes KönigreichMajestic Books
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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
- Print-on-Demand
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