Articles liés à Data Science Solutions with Python: Fast and Scalable...
Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn - Couverture souple
Synopsis
Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process.
The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.
The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.
This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics.
What You Will Learn- Understand widespread supervised and unsupervised learning, including key dimension reduction techniques
- Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning
- Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks
- Design, build, test, and validate skilled machine models and deep learning models
- Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration
Who This Book Is For
Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Tshepo Chris Nokeri harnesses advanced analytics and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor's degree in information management. Afterward, he graduated with an Honours degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 35,77
EUR 4,56 expédition depuis Royaume-Uni vers France
Destinations, frais et délaisRésultats de recherche pour Data Science Solutions with Python: Fast and Scalable...
Image d'archives
Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9781484277614_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Data Science Solutions With Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2o, XGbBost, and Scikit-Learn
Edité par
Apress, 2021
ISBN 10 : 1484277619
ISBN 13 : 9781484277614
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 43689087
Quantité disponible : 5 disponible(s)
Image fournie par le vendeur
Data Science Solutions with Python
Edité par
Springer, Berlin|Apress, 2022
ISBN 10 : 1484277619
ISBN 13 : 9781484277614
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced user levelApply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and a. N° de réf. du vendeur 501191693
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Data Science Solutions With Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2o, XGbBost, and Scikit-Learn
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 43689087-n
Quantité disponible : 5 disponible(s)
Image d'archives
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
PF. Etat : New. N° de réf. du vendeur 6666-IUK-9781484277614
Quantité disponible : 10 disponible(s)
Image d'archives
Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
impression à la demandeVendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 209. N° de réf. du vendeur C9781484277614
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Data Science Solutions With Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2o, XGbBost, and Scikit-Learn
Edité par
Apress, 2021
ISBN 10 : 1484277619
ISBN 13 : 9781484277614
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 43689087
Quantité disponible : 5 disponible(s)
Image fournie par le vendeur
Data Science Solutions with Python
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process.The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics.What You Will LearnUnderstand widespread supervised and unsupervised learning, including key dimension reduction techniquesKnow the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learningIntegrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworksDesign, build, test, and validate skilled machine models and deep learning modelsOptimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alterationWho This Book Is ForData scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics 136 pp. Englisch. N° de réf. du vendeur 9781484277614
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Data Science Solutions with Python : Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process.The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics.What You Will LearnUnderstand widespread supervised and unsupervised learning, including key dimension reduction techniquesKnow the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learningIntegrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworksDesign, build, test, and validate skilled machine models and deep learning modelsOptimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alterationWho This Book Is ForData scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics. N° de réf. du vendeur 9781484277614
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Data Science Solutions with Python
Edité par
Apress, Apress Okt 2021, 2021
ISBN 10 : 1484277619
ISBN 13 : 9781484277614
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 136 pp. Englisch. N° de réf. du vendeur 9781484277614
Quantité disponible : 2 disponible(s)