Isbn: 9781138315068 - advanced data science and analytics with python (5 risultati)

Lingua: Inglese
Editore: Chapman & Hall, 2020
Serie: Libro 52 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Lingua: Inglese
Editore: CRC Press, 2020
Serie: Libro 52 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Altre immaginiLingua: Inglese
Editore: Taylor & Francis, 2020
Serie: Libro 52 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Taschenbuch. Condizione: Neu. Advanced Data Science and Analytics with Python | Jesús Rogel-Salazar | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Taylor & Francis | EAN 9781138315068 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu. …

Lingua: Inglese
Editore: Chapman And Hall/CRC Mai 2020, 2020
Serie: Libro 52 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.Features:Targets readers with a background in programming, who are interested in the tools used in data analytics and data scienceUses Python throughoutPresents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needsFocuses on the practical use of the tools rather than on lengthy explanationsProvides the reader with the opportunity to use the book whenever needed rather than following a sequential pathThe book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products andaddresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app.About the AuthorDr. Jesús Rogel-Salazar isa lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. 424 pp. Englisch.…

Lingua: Inglese
Editore: Chapman And Hall/CRC, 2020
Serie: Libro 52 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Brossura
- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.Features:Targets readers with a background in programming, who are interested in the tools used in data analytics and data scienceUses Python throughoutPresents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needsFocuses on the practical use of the tools rather than on lengthy explanationsProvides the reader with the opportunity to use the book whenever needed rather than following a sequential pathThe book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products andaddresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app.About the AuthorDr. Jesús Rogel-Salazar isa lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.…