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Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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paperback. Condition: Good. 2nd ed. Minor wear on the corners. Clean pages with no markings. Ships same day in most cases!The image in this listing is stock photo for reference. Actual item may differ. Any queries, just ask for photos. Your satisfaction matters!
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paperback. Condition: New. 2nd Revised edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Machine Learning and Data Science, 2nd Edition
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
Language: English
Published by Packt Publishing 2021-07-29, 2021
ISBN 10: 1800564481 ISBN 13: 9781800564480
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The Data Science Workshop: Learn how you can build machine learning models and create your own real-world data science projects, 2nd Edition
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Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition
Language: English
Published by Packt Publishing 2018-10-31, 2018
ISBN 10: 1789806070 ISBN 13: 9781789806076
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Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python, 2nd Edition
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Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
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Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Language: English
Published by Packt Publishing 2018-08-30, 2018
ISBN 10: 1789347998 ISBN 13: 9781789347999
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Taschenbuch. Condition: Neu. ICDSMLA 2020 | Proceedings of the 2nd International Conference on Data Science, Machine Learning and Applications | Amit Kumar (u. a.) | Taschenbuch | Lecture Notes in Electrical Engineering | 2 Taschenbücher | Englisch | 2022 | Springer | EAN 9789811636929 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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ICDSMLA 2020 : Proceedings of the 2nd International Conference on Data Science, Machine Learning and Applications
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811636923 ISBN 13: 9789811636929
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human-computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
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Add to basketHardcover. Condition: Brand New. 1691 pages. 9.25x6.10x3.46 inches. In Stock.
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Morphological variability and standardisation of vessel shapes in the 2nd and first half of the first millennium BC in continental Italy : a data science and machine learning project for the quantitative study of archaeological ceramic profiles :
Published by Edipuglia, Bari, 2024
ISBN 13: 9791259950840
Seller: Libreria già Nardecchia s.r.l., Rome, RM, Italy
Seller rating 4 out of 5 stars
Bari: Edipuglia, 2024 9791259950840 Adrias 18 400 N vol.: 1, N pag.: 180, Peso: 400, L: 28, W: 22.5, Illustr.: COL + B/N "È una soddisfazione particolare presentare il primo volume della nuova serie del Dottorato di Interesse Nazionale.
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Brossura. Condition: nuovo. edipuglia, 2024. ISBN: 979-12-5995-084-0. Pagine: 180, ill. b/n e col. -- Introduzione Preface Acknowledgements Conventions used in this work Chapter 1 Introduction 1.1 The reason for this work 1.2 The study of variability and standardization 1.3 The relationship with specialization 1.4 Historical and archaeological background, phenomena of standardisation, craft specialisation and social complexity in protohistoric and preroman Italy 1.5 Challenges and limits in the dataset and approach 1.6 Informatics and archaeology 1.7 Reflections on the approach used: digital archaeology, open data, and Big Data 1.8 Machine learning and related disciplines Chapter 2 The dataset 2.1 Introduction 2.2 Methodology of dataset creation 2.3 Distribution maps, references, and other information 2.4 Assignment of records of uncertain chronology 2.5 AIPPD, Big Data applied in archaeology? 2.6 AIPPD: conclusions and perspectives 2.7 Timelines and time considerations for the dataset Chapter 3 Methods 3.1 Python and AIPPA, an overview 3.2 potds: a framework for working with profiles 3.3 Features extraction: transforming images into numerical data Chapter 4 Analysis techniques: methodologies and examples 4.1 Dimensionality reduction 4.2 Clustering algorithms 4.3 Proposal of a pipeline for the study and analysis of vessels' profile 4.4 Traditional typology and proposed pipeline, what relationship? 4.5 Hardware used Chapter 5 Applications 5.1 Comparison between Features extraction methods: a benchmark 5.2 Complexity in funeral urns and vascular production between EBA and AP 5.3 Standardisation analysis: a proposal of a method and its application: EBA AP 5.4 Pottery assemblages maps 5.5 Similarity of contexts: a proposal for approaches 5.6 Reconstructing entire vessels from fragments: an ML approach Chapter 6 General conclusions and future prospects References.
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Paperback. Condition: new. Paperback. Build real-world machine learning solutions from scratch using R-no advanced math or prior coding experience required.This second edition of Machine Learning and Data Science offers an accessible, hands-on introduction to the core principles of machine learning, statistical modeling, and practical data science-without overwhelming readers with complex formulas or technical jargon. Perfect for beginners, analysts, and business professionals transitioning into data science, this book provides a complete project-based roadmap from data wrangling to model deployment using the powerful R programming language. Whether you're analyzing marketing trends, predicting customer behavior, or detecting fraud, this book equips you with the foundation needed to solve real problems using machine learning.Author and data scientist Daniel D. Gutierrez draws on his experience teaching at UCLA and years of industry practice to guide you through essential topics, including regression, classification, clustering, feature engineering, and model evaluation. You'll explore supervised and unsupervised learning techniques, apply visualization strategies, and build intuitive workflows that mirror the data science process used by professionals across finance, healthcare, marketing, and more. Unlike overly theoretical texts, this guide emphasizes application-what to do, why to do it, and how to do it in R.Inside, you'll find step-by-step tutorials, use case examples from Kaggle competitions, and easy-to-follow code snippets that let you apply machine learning concepts immediately. Learn how to access and clean real-world data sets, implement algorithms like decision trees, random forests, logistic regression, and k-means clustering, and avoid common pitfalls such as data leakage and overfitting. Move from exploratory data analysis to powerful predictive modeling.Whether you're a student, aspiring data scientist, or working analyst seeking to expand your skills, this is your essential, beginner-friendly guide to statistical learning and machine learning with R. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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paperback. Condition: New. Paperback. Pub Date: 2023-10 Pages: 356 Language: Chinese Publisher: Renmin University of China Press Machine learning is an indispensable and important part of data science and one of the core contents of data science talent training. This book uses a trinity explanation method of machine learning theory + Python programming + application practice to lead readers into the field of Python machine learning. Theoretically. it highlights the readability of machine learning theory explanations an.
