Adithya krishnan (38 résultats)
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 24,27
EUR 2,31 expéditionExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 26,07
EUR 2,31 expéditionExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : As New. Unread book in perfect condition.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Couverture souple
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 29,71
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : 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
- Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 27,49
EUR 2,31 expéditionExpédition nationale : Etats-UnisQuantité disponible : 4 disponible(s)
Etat : New.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Couverture souple
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 29,87
Frais de port gratuitsExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 recommende…r 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
- Couverture souple
- Édition originale
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-UnisRarewaves USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 31,76
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 li…ke 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
- Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 31,29
EUR 2,31 expéditionExpédition nationale : Etats-UnisQuantité disponible : 4 disponible(s)
Etat : 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
- Couverture souple
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 34,21
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Couverture souple
- Édition originale
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 34,74
Frais de port gratuitsExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 li…ke 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
- Couverture souple
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-UnisRarewaves USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 38,67
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 recommende…r 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
- Couverture souple
- Édition originale
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandeKennys Bookshop and Art Galleries Ltd.
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 35,26
EUR 10,50 expéditionExpédition depuis Irlande vers Etats-UnisQuantité disponible : 15 disponible(s)
Etat : 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
- Couverture souple
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 37,83
EUR 11,71 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Paperback. Etat : 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
- Couverture souple
Vendeur : Ria Christie Collections, Uxbridge, Royaume-UniRia Christie Collections
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 35,94
EUR 14,02 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. In.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 32,27
EUR 17,56 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Etat : As New. Unread book in perfect condition.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 33,43
EUR 17,56 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Etat : New.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R, Shivananda, Adarsha, Kulkarni, Anoosh, Krishnan, V Adithya
- Couverture souple
Vendeur : Kennys Bookstore, Olney, MD, Etats-UnisKennys Bookstore
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 43,58
EUR 9,18 expéditionExpédition nationale : Etats-UnisQuantité disponible : 15 disponible(s)
Etat : 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
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 37,24
EUR 17,56 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : 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
- Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 38,41
EUR 17,56 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : 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,
- Couverture souple
Vendeur : Chiron Media, Wallingford, Royaume-UniChiron Media
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 39,47
EUR 18,13 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
paperback. Etat : 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
- Couverture souple
Vendeur : Ria Christie Collections, Uxbridge, Royaume-UniRia Christie Collections
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 46,44
EUR 14,02 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. In.
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
- Couverture souple
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 46,36
EUR 14,63 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Paperback. Etat : 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
- Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 58,69
EUR 3,49 expéditionExpédition nationale : Etats-UnisQuantité disponible : 4 disponible(s)
Etat : 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
- Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 72,99
EUR 3,49 expéditionExpédition nationale : Etats-UnisQuantité disponible : 4 disponible(s)
Etat : New. 1st ed. edition NO-PA16APR2015-KAP.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Couverture souple
- Édition originale
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-UnisRarewaves USA United
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 33,48
EUR 43,70 expéditionExpédition nationale : Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 li…ke 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
- Couverture souple
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 32,41
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Couverture souple
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-UnisRarewaves USA United
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 40,47
EUR 43,70 expéditionExpédition nationale : Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 recommende…r 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.
- Couverture souple
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 26,03
EUR 61,36 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : Neu. Neuware - Drowning in the digital noise This isn't another preachy self-help book. It's not a spiritual sermon wrapped in Sanskrit, and definitely not about quitting social media to live in a cave.
- Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 37,35
EUR 62,18 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Buch. Etat : Neu. Neuware - Drowning in the digital noise This isn't another preachy self-help book. It's not a spiritual sermon wrapped in Sanskrit, and definitely not about quitting social media to live in a cave.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Couverture souple
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 26,95
EUR 76,09 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : 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 recommende…r 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
- Couverture souple
- Édition originale
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 33,42
EUR 76,09 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 8 disponible(s)
Paperback. Etat : 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 li…ke 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.
