Machine Learning Models Algorithms by Suthaharan Shan (31 results)
Language: English
Published by Springer, 2019
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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hardcover. Condition: Good. 1st ed. 2016. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2015
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Condition: New. pp. 359.
Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Taschenbuch. Condition: Neu. Machine Learning Models and Algorithms for Big Data Classification | Thinking with Examples for Effective Learning | Shan Suthaharan | Taschenbuch | Integrated Series in Information Systems | xix | Englisch | 2016 | Springer | EAN 9781489978523 | Verantwortliche Person für die EU: Springer Verlag Gmb…H, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer-Verlag New York Inc., New York, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Hardcover. Condition: new. Hardcover. This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly s…uitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems. This book presents machine learning models and algorithms to address big data classification problems. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Springer US, Springer New York, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learn…ing (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Language: English
Published by Springer US, Springer US, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and dee…p learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Paperback. Condition: Brand New. reprint edition. 359 pages. 9.25x6.10x0.90 inches. In Stock.
Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer, 2015
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Language: English
Published by Springer-Verlag New York Inc., New York, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Hardcover. Condition: new. Hardcover. This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly s…uitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems. This book presents machine learning models and algorithms to address big data classification problems. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Language: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Language: English
Published by Springer US, Springer US Okt 2015, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach)…, and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems. 380 pp. Englisch.
Language: English
Published by Springer US, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Addresses a new and hot field of Big Data Science and Engineering Offers new Machine Learning techniques and solutions Provides solutions to overcome Big Data classification problems that industries, government agenci…es and organizations st.
Language: English
Published by Springer US, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Addresses a new and hot field of Big Data Science and Engineering Offers new Machine Learning techniques and solutions Provides solutions to overcome Big Data classification problems that industries, governm…ent agencies and organizations st.
Language: English
Published by Springer US, Springer US Aug 2016, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical ap…proach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems. 380 pp. Englisch.
Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Condition: New. Print on Demand pp. 359.
Language: English
Published by Springer, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
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Condition: New. PRINT ON DEMAND pp. 359.
More imagesLanguage: English
Published by Springer, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Buch. Condition: Neu. Machine Learning Models and Algorithms for Big Data Classification | Thinking with Examples for Effective Learning | Shan Suthaharan | Buch | Integrated Series in Information Systems | xix | Englisch | 2015 | Springer | EAN 9781489976406 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartens…tr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Language: English
Published by Springer US, Springer New York Okt 2015, 2015
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Hardcover
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), an…d deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems.The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch.
Language: English
Published by Springer US, Springer US Aug 2016, 2016
Series: Integrated Series in Information Systems, Book 36 of 40. Book 36 of 40 - Integrated Series in Information Systems
- Softcover
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approa…ch), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems.The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch.
