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ANGEWANDTES MASCHINELLES LERNEN FÜR DATA SCIENCE PRAKTIKER-
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Standort: Lansdale, PA, USA
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eBay-Artikelnr.:357418534769
Artikelmerkmale
- Artikelzustand
- Educational Level
- Adult & Further Education
- Country/Region of Manufacture
- United States
- ISBN
- 9781394155378
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1394155379
ISBN-13
9781394155378
eBay Product ID (ePID)
10069447042
Product Key Features
Number of Pages
656 Pages
Language
English
Publication Name
Applied Machine Learning for Data Science Practitioners
Publication Year
2025
Subject
Probability & Statistics / General, General
Type
Textbook
Subject Area
Mathematics, Science
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
52.3 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2025-021296
Dewey Edition
23
Dewey Decimal
006.31
Table Of Content
About the Author xix How do I Use this Book? xxi Foreword xxv Preface xxvi Acknowledgments xxvii About the Companion Website xxix Section 1: Introduction to Machine Learning and Data Science 1 Data Science Overview 3 Section 2: Data Preparation and Feature Engineering 2 Data Preparation 31 3 Data Extraction 39 4 Machine Learning Problem Framing 57 5 Data Comprehension 75 6 Data Quality Engineering 135 7 Feature Optimization 173 8 Feature Set Finalization 183 Section 3: Build, Train, or Estimate the ML Model 9 Regression 211 10 Classification 279 11 Ranking 333 12 Clustering 357 13 Patterns 381 14 Time Series 401 15 Anomaly Detection 457 Section 4: Model Performance Optimization 16 Model Optimization & Model Selection 483 17 Decision Tree 507 18 Ensemble Methods 533 Section 5: ML Ethics 19 ML Ethics 569 Section 6: Productionalize the Machine Learning Model 20 Deploy and Monitor Models 599 Index 615
Synopsis
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift., A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). This book provides an excellent, practical compendium of the foundational topics in data science and machine learning, from a true expert. This book shows how Data Science and Machine Learning fit together in a workflow --- and learning that workflow is an essential foundation for building ML systems. I highly recommend this book for anyone who wants to master the fundamentals of building and analyzing ML models. Dr Anoop Sinha (Research Director, Google) Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.
LC Classification Number
Q325.5.S8 2025
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