Applied Machine Learning for Data Science Practitioners - Vidya Subramanian

. .

Auteur(s) : Vidya Subramanian

Editeur : John Wiley & Sons Inc

Langue : Anglais

Parution : 01/03/2025

Format : Moyen, de 350g à 1kg

Nombre de pages : 656.0

ISBN : 1394155379

Résumé :

Vidya Subramanian is a passionate Data Science and Analytics leader, with experience leading teams at Google, Apple, and Intuit. Forbes recognized her as one of the 8 Female Analytics Experts From The Fortune 500. She authored Adobe Analytics with SiteCatalyst (Adobe Press) and McGraw-Hill's PMP Certification Mathematics (McGraw Hill). Vidya holds Master's degrees from Virginia Tech and Somaiya Institute of Management (India) and currently leads Data Science and Analytics for Google Play.

...

Biographie:

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

Sommaire:

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.