Principles of Data Science - A Beginner's Guide to Essential Math and Coding Skills for Data Fluency and Machine Learning

This book bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insig...

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Bibliographische Detailangaben
1. Verfasser: Ozdemir, Sinan
Format: E-Book Buch
Sprache:Englisch
Veröffentlicht: Birmingham Packt Publishing 2024
Packt Publishing, Limited
Ausgabe:3
Schlagworte:
Business > Data processing
Data mining
General Engineering & Project Administration
General References
Quantitative research
Software Engineering
ISBN:9781837636303, 1837636303
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This book bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.
Bibliographie:Content Type: text (ncrcontent), Media Type: unmediated (ncrmedia), Carrier Type: volume (ncrcarrier)
Includes index
ISBN:9781837636303
1837636303
DOI:10.0000/9781837636006